Deployment Strategist Lead Jobs

About the Team The Agent Enablement team works across engineering, product, design, and research to bring our technology to the world. We seek to learn from deployment and broadly distribute the benefits of AI, while ensuring that this powerful tool is used responsibly and safely. We aim to make our innovative tools globally accessible, transcending geographic, economic, and platform barriers. Our commitment is to facilitate the use of AI to enhance lives, supported by rigorous insights into how people use our products. About the Role We are looking for experienced full-stack engineers to join our new Agent Enablement team. Our goal is to design and grow an open ecosystem of agent-enabled sites and services. This is a wide-ranging role: you’ll build new user and agent identity protocols, user experiences to control and observe agents across web, desktop, and mobile, and much more. We will rely on you to drive our technical decisions while also steering our product and partnership direction, optimizing for both short-term impact and long-term success of the ecosystem. We value engineers who are impact-driven, autonomous, and adept at removing barriers to forward progress. In this role, you will: Design the primitives and protocols for an open agent ecosystem, enabling our users’ agents to make the best use of sites and services across the internet. Build a next-generation user experience to observe and control agents, across web, desktop, and mobile. Evolve our approach to token consumption across subscriptions and API customers. Execute on fast-paced projects in collaboration with research, design, data science and other product engineering teams. Work closely with our strategic customers and partners to grow the ecosystem. You might thrive in this role if you: Have strong full-stack engineering skills and experience shipping customer-facing products from concept to production. You’re comfortable working across frontend, backend, APIs, data models, and product desig

We are seeking an experienced Quantitative Developer to join our Markets Quantitative Analytics team, partnering closely with Quantitative Analysts, Traders, and Technology professionals to build the next generation of pricing, risk, and analytics platforms. This is a hands-on technical role for a highly skilled software engineer with a passion for quantitative finance. You will be responsible for designing and delivering high-performance, scalable solutions that support front office trading businesses across asset classes. The role offers the opportunity to work on complex quantitative challenges, modern engineering practices, and large-scale distributed systems while helping shape the strategic direction of Citi's quantitative technology platform. Successful candidates will combine strong software engineering expertise with an understanding of quantitative methodologies and financial markets, translating sophisticated mathematical models into robust, production-grade solutions. Key Responsibilities Design, develop, and maintain high-performance pricing, risk, and analytics libraries used across Global Markets. Partner with Quantitative Analysts to transform research models and prototypes into scalable, production-quality software. Build and optimize quantitative applications using modern C++ and Python, applying strong software architecture and engineering principles. Own the full software development lifecycle, including requirements gathering, design, implementation, testing, deployment, and ongoing support. Drive engineering excellence through CI/CD adoption, automated testing, code reviews, and software quality best practices. Develop and maintain market data platforms and data pipelines supporting analytics, pricing, and risk workflows. Work with infrastructure teams to leverage distributed computing, cloud technologies, and scalable arc

We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time. The Release Engineer is responsible for implementing and supporting automated software delivery processes that enable reliable, secure, and repeatable deployments across development, test, non-production, and production environments. This role serves as a technical bridge between Software Engineering, Quality Engineering, Infrastructure, and Operations teams to improve deployment speed, stability, and operational efficiency. The Release Engineer is responsible for implementing and supporting automated software delivery processes that enable reliable, secure, and repeatable deployments across development, test, non-production, and production environments. This role serves as a technical bridge between Software Engineering, Quality Engineering, Infrastructure, and Operations teams to improve deployment speed, stability, and operational efficiency. Key Responsibilities Develop, support, and maintain automated CI/CD pipelines to streamline application delivery. Standardize build, release, and deployment processes across applications and platforms. Establish repeatable, auditable, and traceable release management practices to ensure deployment consistency and compliance. Manage and support Git-based source control, branching strategies, and release workflows. Integrate automated testing, quality gates, security controls, and compliance checks into deploym

About DevRev At DevRev, we're building the future of work with Computer – your AI teammate. Unlike traditional tools, Computer unifies all your data sources, tools, and workflows into a single AI-ready platform, giving employees real-time insights, proactive suggestions, and powerful agentic actions. It extends your existing software with AI-native apps and agents that work alongside your teams and customers – updating workflows, coordinating across teams, and eliminating repetitive work. We call this Team Intelligence: human-AI collaboration that breaks down silos, brings people back together, and frees you to solve bigger problems. Backed by Khosla Ventures and Mayfield with $150M+ raised, DevRev is trusted by global companies across industries. What You’ll Do: Architect the Future of AI Infrastructure: You will design, build, and own the end-to-end platform that supports the entire lifecycle of our ML models—from massive-scale distributed training to ultra-low-latency, highly-available inference. Optimize and Serve Cutting-Edge Models: You'll implement and scale sophisticated inference stacks for LLMs using frameworks like vLLM, TensorRT-LLM, or SGLang . You’ll solve complex challenges in throughput, latency, token streaming, and automated scaling to deliver a seamless user experience. Empower AI Innovation: You will act as a strategic partner to our AI Research and Data Science teams. You’ll create a seamless developer experience that accelerates their ability to experiment, fine-tune, and deploy groundbreaking models with velocity and confidence. Automate Everything: You'll develop robust CI/CD/CT (Continuous Training) pipelines using tools like Argo Workflows, ArgoCD, and GitHub Actions to automate model validation, deployment, and lifecycle management, ensuring our systems are both agile and rock-solid. What are we looking for Experience: 5+ years in infrastructure or software engineering, with at least 2+ years laser-focused on MLOps or ML infrastructu

About DevRev At DevRev, we're building the future of work with Computer – your AI teammate. Unlike traditional tools, Computer unifies all your data sources, tools, and workflows into a single AI-ready platform, giving employees real-time insights, proactive suggestions, and powerful agentic actions. It extends your existing software with AI-native apps and agents that work alongside your teams and customers – updating workflows, coordinating across teams, and eliminating repetitive work. We call this Team Intelligence: human-AI collaboration that breaks down silos, brings people back together, and frees you to solve bigger problems. Backed by Khosla Ventures and Mayfield with $150M+ raised, DevRev is trusted by global companies across industries. About the Role: As a Forward Deployment Architect, you will serve as a hands-on senior technical architect on the Applied AI Engineering team, owning the end-to-end design and delivery of AI-driven business transformation projects. You'll work closely with pre-sales teams to scope technical integration and implementation strategies, translating business requirements into architectural solutions. Once opportunities move to post-sales, you'll own the detailed technical designs from original scoping documents and drive execution including hands-on coding to build proofs-of-concept, custom integrations, and solution prototypes that validate technical feasibility. As the technical owner of the customer relationship, you'll partner cross-collaboratively to ensure successful delivery. Your role spans from understanding domain-specific customer needs to architecting scalable, agent-based AI solutions using DevRev's platform**, with direct involvement in implementing key technical components and debugging complex integration challenges. This position requires a unique blend of enterprise architecture expertise, AI solution design, hands-on development skills, customer empathy, and cross-functional collaboration. You'll act as

Job Overview: We are looking for a Senior GenAI Developer to design, build, and productionize agentic AI systems—LLM-powered agents that can plan, use tools, orchestrate workflows, and operate reliably under enterprise constraints. You will own key parts of the agent architecture (planning, tool use, memory, evaluation, safety/guardrails, and observability) and deliver end-to-end solutions across RAG, function/tool calling, multi-agent coordination, and scalable deployment. Key Responsibilities Design and implement agentic systems: single-agent and multi-agent architectures (planner/executor, supervisor-worker, routing, reflection, critique, task decomposition). Build robust tool-using agents: function calling, tool schemas, tool authorization, retries, rate limiting, and sandboxing. Implement RAG + memory patterns: retrieval strategies, hybrid search, context assembly, long-term memory, and grounding/citation behaviors. Develop workflow orchestration for agent execution (state machines/graphs), concurrency controls, and deterministic execution where possible. Productionize GenAI services: APIs, background jobs, streaming responses, caching, and cost/latency optimization. Establish agent evaluation: golden sets, simulation-based evals, LLM-as-judge with mitigations, task success metrics, regression testing. Build observability and safety: tracing, token/tool telemetry, anomaly detection, prompt injection defenses, data leakage prevention, policy enforcement. Collaborate with product, security, and platform teams to deliver enterprise-ready solutions and integrate with internal systems (data, identity, workflow). Mentor engineers, set coding standards, and contribute to architecture reviews and technical roadmaps. Required Qualifications 6+ years software engineering experience; 2+ years building LLM/GenAI systems in production. Strong programming skills in Python (required) and/or TypeScript/Node.js. H

About the Role We are seeking an experienced Azure DevOps Engineer to design, implement, and maintain CI/CD pipelines, cloud infrastructure, and automation solutions on Microsoft Azure. This role bridges development and operations, ensuring reliable, secure, and scalable delivery of applications and infrastructure. Location: Hyderabad-India-Onsite Duration: Fulltime Responsibilities Design, build, and maintain CI/CD pipelines using Azure DevOps (Pipelines, Repos, Artifacts, Boards) Architect and manage Azure cloud infrastructure using Infrastructure as Code (ARM templates, Bicep, or Terraform) Automate build, test, and deployment processes across multiple environments Implement and manage containerization and orchestration (Docker, Azure Kubernetes Service) Monitor system performance, availability, and security using Azure Monitor, Log Analytics, and Application Insights Collaborate with development, QA, and security teams to streamline release management Implement Azure security best practices, identity management (Azure AD/Entra ID), and network architecture Manage cost optimization and governance across Azure subscriptions Troubleshoot production issues and support incident response Document infrastructure, pipelines, and operational procedures Required Qualifications Microsoft Certified: Azure Solutions Architect Expert, Azure Administrator Associate (required) 3+ years of hands-on experience with Azure DevOps and Azure cloud services Strong experience with Infrastructure as Code (Bicep, ARM templates, or Terraform) Proficiency scripting in PowerShell, Bash, or Python Experience with Git version control and branching strategies Solid understanding of networking, security, and identity concepts in Azure Experience with containerization (Docker) and orchestration (Kubernetes/AKS) Familiarity with monitoring and logging tools (Azure Monitor, Application Insights) Preferred Qualifications Additional certifications: Azure DevOps Engineer Expert Experience with multi-

SDLC Engineer A Career with Point72’s Technology Team As Point72 reimagines the future of investing, our Technology group is constantly improving our company’s IT infrastructure, positioning us at the forefront of a rapidly evolving technology landscape. We’re a team of experts experimenting, discovering new ways to harness the power of open-source solutions, and embracing enterprise agile methodology. We encourage professional development to ensure you bring innovative ideas to our products while satisfying your own intellectual curiosity. What You’ll Do Provide expert technical support for our enterprise Software Development Life Cycle (SDLC) platforms including Jenkins, GitHub, Bitbucket, and AWS-based CI/CD pipelines Support and optimize our artifact management solutions (Artifactory) and static code analysis tools (SonarQube) Partner directly with business teams and development clients to address SDLC platform challenges and deliver effective solutions Implement and maintain security controls across our development toolchain and infrastructure Develop automation solutions to eliminate toil and enhance developer productivity Troubleshoot complex build, deployment, and integration issues across our development environments Contribute to continuous improvement of our AWS-based development infrastructure Maintain documentation and knowledge base for supported platforms and tools What’s required 5+ years of experience in software engineering, DevOps, or SRE roles Strong technical expertise in AWS services and cloud-native architectures Experience with container technologies (Docker, ECS, EKS) and container orchestration Deep understanding of Git workflows, branching strategies, and version control best practices Strong hands-on programming/scripting skills (Python, Go, or similar), with ability to debug and build automation for CI/CD pipelines Experience with infrastructure as code (Terraform, CloudFormation) in AWS environments Hands-on experience with CICD solutio

About the Role Adobe is seeking a Machine Learning Engineer to join the Adobe Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse, and misuse using intelligent systems worldwide. In this position, you will build and develop machine learning models from scratch, including custom transformer-based frameworks, to identify fraudulent actions, stop account sharing, and protect the experience of hundreds of millions of users. You will manage the entire model lifecycle: raw behavioral data and feature engineering, architecture development, large-scale GPU training, deployment, and monitoring. The team is actively building in-house behavioral foundation models that learn identity-preserving representations from long sequences of user activity. This is a role for an engineer who wants to own deep learning systems end-to-end — not consume pre-built ones. Key Responsibilities Build and train deep learning models from scratch, including custom transformer and attention-based architectures for long behavioral event sequences. Own the full training stack: event tokenization, temporal and positional embeddings, self-supervised pretraining (e.g., masked modeling, contrastive learning), and downstream fine-tuning. Train large models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation/checkpointing, efficient attention, and distributed strategies (DDP, FSDP, or equivalent). Build and optimize feature pipelines on Databricks and Spark, transforming raw behavioral events into high-quality model inputs. Translate prototypes into production ML systems — scalable, reliable, and observable — and drive inference performance through architectural and serving-side optimization. Contribute to MLOps practices: experiment tracking, model versioning, CI/CD, automated retraining, and

About the Role Adobe is seeking a Machine Learning Engineer to join the Adobe Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse, and misuse using intelligent systems worldwide. In this position, you will build and develop machine learning models from scratch, including custom transformer-based frameworks, to identify fraudulent actions, stop account sharing, and protect the experience of hundreds of millions of users. You will manage the entire model lifecycle: raw behavioral data and feature engineering, architecture development, large-scale GPU training, deployment, and monitoring. The team is actively building in-house behavioral foundation models that learn identity-preserving representations from long sequences of user activity. This is a role for an engineer who wants to own deep learning systems end-to-end — not consume pre-built ones. Key Responsibilities Build and train deep learning models from scratch, including custom transformer and attention-based architectures for long behavioral event sequences. Own the full training stack: event tokenization, temporal and positional embeddings, self-supervised pretraining (e.g., masked modeling, contrastive learning), and downstream fine-tuning. Train large models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation/checkpointing, efficient attention, and distributed strategies (DDP, FSDP, or equivalent). Build and optimize feature pipelines on Databricks and Spark, transforming raw behavioral events into high-quality model inputs. Translate prototypes into production ML systems — scalable, reliable, and observable — and drive inference performance through architectural and serving-side optimization. Contribute to MLOps practices: experiment tracking, model versioning, CI/CD, automated retraining, and

About the Role Adobe is seeking a Machine Learning Engineer to join the Adobe Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse, and misuse using intelligent systems worldwide. In this position, you will build and develop machine learning models from scratch, including custom transformer-based frameworks, to identify fraudulent actions, stop account sharing, and protect the experience of hundreds of millions of users. You will manage the entire model lifecycle: raw behavioral data and feature engineering, architecture development, large-scale GPU training, deployment, and monitoring. The team is actively building in-house behavioral foundation models that learn identity-preserving representations from long sequences of user activity. This is a role for an engineer who wants to own deep learning systems end-to-end — not consume pre-built ones. Key Responsibilities Build and train deep learning models from scratch, including custom transformer and attention-based architectures for long behavioral event sequences. Own the full training stack: event tokenization, temporal and positional embeddings, self-supervised pretraining (e.g., masked modeling, contrastive learning), and downstream fine-tuning. Train large models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation/checkpointing, efficient attention, and distributed strategies (DDP, FSDP, or equivalent). Build and optimize feature pipelines on Databricks and Spark, transforming raw behavioral events into high-quality model inputs. Translate prototypes into production ML systems — scalable, reliable, and observable — and drive inference performance through architectural and serving-side optimization. Contribute to MLOps practices: experiment tracking, model versioning, CI/CD, automated retraining, and

Replit is the agentic software creation platform that enables anyone to build applications using natural language. With millions of users worldwide, Replit is democratizing software development by removing traditional barriers to application creation. About the role: Join our FDE team and work directly with some of the world's largest organizations to turn their most ambitious ideas into production applications on Replit. As a Forward Deployed Engineer, you'll partner closely with customers to understand their technical and business needs, architect solutions, and build the integrations and applications required to deploy Replit successfully within complex enterprise environments. This is a deeply technical and hands-on role. You won’t just advise customers on what to build—you’ll build alongside them. You’ll take applications from initial idea and prototype through deployment and production, navigate complex enterprise environments, and solve the technical challenges that emerge when AI-powered software development meets real-world infrastructure, data, security, and organizational constraints. You will: Build with Customers: Embed with strategic enterprise customers to design and build high-impact applications and AI-powered workflows on Replit, taking projects from initial concept through production deployment. Architect Enterprise Solutions: Design secure, scalable architectures that connect Replit with customers’ existing systems, data, APIs, identity providers, and infrastructure. Integrate with the customer's stack: SSO, data warehouses, internal APIs, and SaaS systems. Own Technical Deployments: Serve as the technical owner for complex enterprise implementations, identifying blockers, debugging issues, and driving projects through to successful production adoption. Bridge Customers and Product: Develop a deep understanding of how enterprises use Replit and translate field insights, technical constraints, and recurring customer needs into actionable feedback

As a Forward Deployed Engineer on the Feature Flags team, you'll partner directly with customers to accelerate their feature flag implementations — from initial architecture consulting through prototype builds to full-scale migrations. This role is for someone who wants to write code with customers, not just advise them. You'll work hands-on inside customer codebases to unblock complex, high-stakes deployments, directly influencing deal velocity and customer success. Working closely with Sales, Solutions, and Engineering, you'll be the technical force that turns a signed contract into a live, adopted implementation. What You'll Do: Serve as the hands-on technical partner for strategic customers implementing Datadog Feature Flags, from pre-sales technical validation through post-sales delivery Consult on flag architecture and implementation approach for complex environments — multi-service, multi-platform, high-scale deployments Build prototype flag implementations directly in customer codebases to prove value and de-risk technical decisions early in the sales cycle Implement flags across diverse and advanced deployment modes (server-side, client-side, edge, mobile, streaming/real-time) tailored to each customer's stack Drive full flag migrations to completion — including legacy system cutover — efficiently and with minimal customer engineering burden Identify patterns across customer implementations and feed them back to Product and Engineering to improve the core product and reduce future implementation time Collaborate closely with Engineering on technical edge cases, product gaps, and implementation tooling Partner with Sales and Solutions to accelerate deal cycles by removing technical risk and uncertainty Who You Are: 5 years of professional software engineering experience, with hands-on coding ability across the stack you're deployed into Experience with feature flagging, experimentation, or config management systems (internal or vendor) Comfortable dropping i

About BlockTech BlockTech is a fast-paced algorithmic trading firm at the frontier of global cryptocurrency derivatives and spot markets. We trade 24/7 across some of the most data-rich, fast-moving venues in finance, and we use that data to build smarter models, sharper signals, and more adaptive systems. Crypto is one of the few markets where a researcher can still meaningfully move the edge. The data is abundant, the microstructure is novel, and the feedback loop between a research idea and live PnL couldn’t be shorter. We’re growing fast, and we’re looking for a Quantitative Researcher with a strong machine learning toolkit to help us push that edge further. The role As a Quantitative Researcher on our trading floor, you’ll own ideas end-to-end from hypothesis and dataset construction through feature engineering, model training and backtesting, all the way to live deployment, monitoring, and iterative improvement. You’ll sit shoulder-to-shoulder with Quantitative Traders and Quantitative Analysts, and your work will directly drive how we price and trade. What you’ll work on Collaborating closely with traders to translate research insights into systematic trading strategies Designing, developing and deploying models for price prediction, signal generation, execution, and anomaly detection across crypto derivatives and spot markets using state-of-the-art AI and ML techniques. Building robust trading, backtesting and research infrastructure alongside our engineers, so promising ideas can move into production quickly and safely Owning models in production: monitoring live performance, diagnosing decay, and iterating on what you ship What we’re looking for A strong academic background in a quantitative discipline (Mathematics, Physics, Statistics, Computer Science, Econometrics, ML/AI, or similar), typically a PhD or an MSc with strong research experience Fluency in Python and the modern ML stack (PyTorch and/or TensorFlow, scikit-learn, NumPy, pandas) A deep, intuit

About BlockTech BlockTech is a fast-paced algorithmic trading firm at the frontier of global cryptocurrency derivatives and spot markets. We trade 24/7 across some of the most data-rich, fast-moving venues in finance, and we use that data to build smarter models, sharper signals, and more adaptive systems. Crypto is one of the few markets where a researcher can still meaningfully move the edge. The data is abundant, the microstructure is novel, and the feedback loop between a research idea and live PnL couldn’t be shorter. We’re growing fast, and we’re looking for a Quantitative Researcher with a strong machine learning toolkit to help us push that edge further. The role As a Quantitative Researcher on our trading floor, you’ll own ideas end-to-end from hypothesis and dataset construction through feature engineering, model training and backtesting, all the way to live deployment, monitoring, and iterative improvement. You’ll sit shoulder-to-shoulder with Quantitative Traders and Quantitative Analysts, and your work will directly drive how we price and trade. You will: Collaborate closely with traders to translate research insights into systematic trading strategies Design, develop and deploy models for price prediction, signal generation, execution, and anomaly detection across crypto derivatives and spot markets using state-of-the-art AI and ML techniques. Build robust trading, backtesting and research infrastructure alongside our engineers, so promising ideas can move into production quickly and safely Own models in production: monitoring live performance, diagnosing decay, and iterating on what you ship What we're looking for A strong academic background in a quantitative discipline (Mathematics, Physics, Statistics, Computer Science, Econometrics, ML/AI, or similar), typically a PhD or an MSc with strong research experience Fluency in Python and the modern ML stack (PyTorch and/or TensorFlow, scikit-learn, NumPy, pandas) A deep, intuitive grasp of overfitting, g