Ml Platform Engineer in India

We are expanding our agentic AI capability and are looking for an AI Engineer to join the team. You will work alongside senior engineers to build and maintain AI systems — contributing to agentic pipelines, retrieval infrastructure, and the integrations that tie these systems together. This is a hands-on implementation role with real ownership of components. You will grow your skills in a fast-moving AI practice, working on production systems that directly affect client outcomes. What This Involves: Build and maintain agentic pipelines and workflows under the guidance of senior engineers: tool use, orchestration, and multi-step reasoning. Implement and tune RAG pipelines — including embedding, chunking strategies, vector retrieval, and retrieval evaluation. Contribute to memory and context layer components: integrating vector databases, supporting knowledge graph pipelines, and helping maintain state management across agentic systems. Write clean, well-tested Python code and participate in code reviews. Debug and improve existing AI systems based on evaluation results and production feedback. Collaborate with data engineers and domain experts to integrate AI components with upstream data sources and downstream applications. Document implementations clearly and contribute to shared internal tooling. Requirements: 2–4 years of software or ML engineering experience, with at least 1 year working with LLMs or AI systems in a professional setting. Working knowledge of LLM APIs (OpenAI, Anthropic, or similar) and at least one agentic or RAG framework (LangChain, LlamaIndex, or equivalent). Solid Python skills and comfort with software engineering basics: version control, testing, REST APIs. Familiarity with vector databases or embedding-based search. Curiosity about agentic AI — you follow developments in the space and are eager to apply new techniques. Excellent communication and collaboration skills — comfortable working across cross-functional and client-facing te

Title: Senior AI QA Engineer Location: Bengaluru (Bangalore) Opportunity: As a Senior AI QA Engineer for our Precision Patient Care Pipeline , you will go beyond traditional functional testing. You will be responsible for building the framework that ensures our clinical insights are accurate, safe, and reliable. This role requires a unique blend of high-level software testing and data engineering to validate complex, non-deterministic medical outputs using a hybrid of automated grading methodologies . Key Responsibilities: Architect Multi-Layered Validation Frameworks: Design and implement structured testing strategies that combine deterministic checks, semantic similarity metrics, and model-based evaluations. Automated Model Grading: Develop systems to evaluate clinical pipeline outputs for faithfulness, safety, and hallucination detection using various automated scoring techniques (e.g., BERTScore, ROUGE, or custom heuristics). Vibe-Driven Development: Leverage agentic AI tools to rapidly prototype complex test harnesses, "red-team" clinical logic, and build internal validation utilities at high velocity. Data Pipeline Integrity: Execute integration and regression tests for data-heavy backend processes, ensuring medical data remains consistent from ingestion to insight generation. Collaborative Strategy: Work closely with Data Scientists and Product Managers to define "Ground Truth" datasets and clinical evaluation rubrics. Root Cause Analysis: Deep-dive into complex system failures to identify whether issues stem from code logic, data drift, or model behavior. Requirements: 6+ years of technical experience in Quality Assurance, with a strong focus on system architecture and backend data validation. Advanced Python Proficiency: Expert-level skills in Python for building custom test scripts and working within AI/ML ecosystems. AI/ML Validation Experience: Proven experience testing model outputs using diverse metrics (e.g., Semantic Similarity, NLP metrics, an

Sigmoid Analytics is a leading Data solutions company backed by Sequoia Capital. We offer best in- end-to-end data value chain spanning across Data Science, Data Engineering and Data Ops. With data and technology at the core of our solutions, we are solving some of the toughest problems out there. Our culture is modelled around expertise and mutual respect with a team first mindset. You’ll work with teams that push the boundaries of what-is-possible and build solutions that energize and inspire. Offices: New York | Dallas | San Francisco | Lima | Bengaluru The below role is for our Bengaluru office. About the Role: We are looking for Associate Manager Analytics who will work on a broad range of data analytics, data visualization and business intelligence problems across a variety of industries. More specifically, you will: • Engage with clients to understand their business context • Understand business processes and map the complete process in visual formats. • Translate business problems into analytical structures and solve using statistical/ML techniques • Manage a team of data analysts to deliver solutions for clients. • Collaborate with a team of data scientists and engineers to embed AI and analytics into the business decision processes. Desired Skills & Competencies: • Developing and enhancing algorithms and models to solve business problem. • Providing end-to-end analysis support across different industry domains and application areas. • Generate data cuts/outputs according to agreed specifications (for example, survey data clean up, weighting data, recoding variables, creating custom tabular views, and running cross-tabulations) • Conducting quantitative analyses and interpreting results • Proficient in visualisation tools such as Power BI, Tableau, QlikView, Spotfire (Any). • Proficient in MS SQL Data

About us: Sigmoid is a Leading Data Solutions company offering best-in-class services in Data Engineering and Data Science. Our 1300+ team is strongly driven by the passion to unravel data complexities. We generate actionable insights and translate them into successful business strategies. We leverage. our expertise in Open Source and Cloud Technologies to develop innovative frameworks catering to specific client needs. Our unique approach has positively influenced the business performance of our Fortune 1000 clients across the globe. We have a particularly strong presence in Advertising Technology, Retail-CPG, and BFSI, wherein we are working with Top-3 players in each of these sectors. We are recognized among the world's most innovative tech companies and have won several awards like the TiE50 Winners (2018) and NASSCOM Emerge 50 (2017), among others. Backed by Sequoia and Qualcomm Ventures, Sigmoid operates out of California- San Francisco and Santa Clara, New Jersey, and Bengaluru, India. Learn more: https://www.sigmoid.com/company/ or https://sigmoid.com/careers/ for careers. ᐧ - Our Data Science team worked with one of the largest Ad Tech agencies in the world to build ML and AI-driven solution for Campaign Management and Audience Segmentation to increase ROI for their advertising campaigns. Please refer the JD below: As a Frontend Lead , you will be responsible for developing cloud-first web applications. You will lead mixed discipline development teams to deliver a product to an agreed scope, time and budget and to a high standard. Responsibilities : Align Sigmoid with key Client initiatives Interface daily with customers across leading Fortune 500 companies to understand strategic requirements Ability to understand business requirements and tie them to technology solutions Build a delivery plan with domain experts and stay on track Excellent experience in Application deve

Sigmoid Analytics is a leading Data solutions company backed by Sequoia Capital. We offer best in- end-to-end data value chain spanning across Data Science, Data Engineering and Data Ops. With data and technology at the core of our solutions, we are solving some of the toughest problems out there. Our culture is modelled around expertise and mutual respect with a team first mindset. You’ll work with teams that push the boundaries of what-is-possible and build solutions that energize and inspire. Offices : New York | Dallas | San Francisco | Lima | Bengaluru The below role is for our Bengaluru office. About the Role : You will work on a broad range of cutting-edge data science and machine learning problems across a variety of industries. You will be engaging with clients to understand their business context. If you are passionate to work on complex unstructured business problems that can be solved using data science and machine learning, we would like to talk to you. Role: ALDS Mandate Skills & Competencies: Experience in Programming (Python, R, SQL, NoSQL, Spark) with ML tools & Cloud Technology (AWS, Azure, GCP) Experience in Python libraries such as NumPy, pandas, scikit-learn, tensor-flow, scapy, scrapy, BERT etc. Good understanding in statistics, and ability to design statistical hypothesis testing to aid formal decision making. Develops predictive models using Machine Learning algorithms (SVM, Random Forest, Neural Network, Decision Tree, Logistic Regression, K-mean Clustering, linear regression, PCA etc.) Engaging with clients, understanding complex problem statements, and offering solutions in the domains of Retail, Pharma, Banking, Insurance, etc. Contribute to internal product development initiatives related to data science. Develop data science roadmap, and guide data scientist to meet their deliverables. Handling end-to-end client AI & analytics programs. Your role will be a combination of hands-on contribution, technical team management, and c

We're hiring an AI Support Engineer to work directly with the founder and build the systems that power customer support at Bolna. This isn't a traditional support role — you'll use AI to make support scale, and you'll partner closely with the business team on the customer conversations that matter most. What you'll do - Work directly with the founder to design and continuously improve how customer support runs at Bolna - Pull and collate data from Intercom to spot patterns, recurring issues, and gaps in how customers are being helped - Build AI-powered workflows that triage, answer, and resolve customer support queries with less manual effort - Design the systems and processes behind a streamlined, scalable support flow — from triage to escalation to resolution - Step in directly on critical customer support situations alongside the business team when it matters - Turn recurring support themes into feedback for product and engineering What we're looking for - 1–3 years of experience in a support, ops, or technical customer-facing role — ideally somewhere that rewarded building your own tools and process, not just following a playbook - Hands-on comfort with AI tools/workflows (prompting, automations, agent builders) — you don't need to be an ML engineer, but you should be someone who reaches for AI to solve a workflow problem - Experience with Intercom or a similar support/helpdesk tool - Sharp, structured communicator — equally comfortable writing to customers and to the founder - Comfortable with ambiguity — this role is being built as you build it Nice to have - Experience setting up support automations, chatbots, or AI agents in a real product company - Familiarity with SQL or basic scripting to pull/analyze support data - Startup experience, especially in a 0-to-1 function

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