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myGwork - LGBTQ+ Business Community Logo myGwork - LGBTQ+ Business Community · IT - Software Development

Lead Machine Learning Engineer

India, Mumbai · Job Posted January 05, 2026
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Job Description

As a Lead Machine Learning Engineer, you will be the hands-on technical owner of ML systems that power large-scale data collection, extraction, enrichment, and understanding of unstructured content. You'll design, build, and operate end-to-end solutions-from feature generation and training to low-latency inference and observability. These solutions will measurably improve coverage, freshness, quality, and unit cost across our data pipelines. Your toolbox spans classical ML, NLP, LLMs/GenAI, Agentic AI, Retrieval-Augmented Generation (RAG) frameworks, and Model Context Protocol (MCP). You will use these to deliver retrieval, extraction, classification, summarization, and autonomous tasking capabilities integrated cleanly into production workflows.

Job Responsibility

  • Convert business goals into a clear AI/ML roadmap for data acquisition, extraction, enrichment, and measurable outcomes
  • Architect and ship scalable ML/NLP/LLM (RAG, embeddings, reranking, Agentic AI, MCP) services with high reliability and efficiency
  • Mentor engineers and data scientists through design/code reviews, setting technical standards and elevating craftsmanship
  • Build and integrate classifiers, transformers, LLMs, and evaluators that process and categorize unstructured data at scale
  • Design, operate, and optimize high-throughput collection pipelines with robust orchestration, messaging, storage, and SLAs
  • Partner with Product, Data Collection Engineering, Platform/SRE, and Security to turn ambiguous needs into phased, observable deliveries
  • Pilot and productionize advances in GenAI, Agentic AI, RAG, and MCP to improve quality, speed, and cost
  • Enforce data governance, privacy, and model transparency with least-privilege IAM, secrets management, and auditability
  • Apply Agile/Lean/Fast-Flow practices to reduce cycle time, raise quality, and remove toil via automation
  • Deliver cloud-native solutions on AWS and GCP using Docker/Kubernetes, autoscaling, and progressive delivery patterns
  • Establish experiment tracking, registries, CI/CD, drift detection, SLIs/SLOs, and runbooks for dependable operations
  • Implement offline/online evals (e.g., nDCG/MRR/precision@k), golden sets, and guardrails for RAG and search relevance
  • Optimize latency and unit cost with caching, batching, distillation, right-sizing, and clear dashboards/alerts
  • Produce concise design docs, ADRs, and playbooks to ensure durable, cross-site knowledge transfer

Requirements

  • Bachelor's, Master's, or PhD in Computer Science, Mathematics, Data Science, or a related field
  • 5+ years of experience in the ML Engineering and Data Science field, with a focus on LLM and GenAI technologies, particularly in data collection and unstructured data processing
  • 1+ years of experience in technical lead position
  • Strong expertise in NLP and machine learning, with hands-on experience in classifiers, large language models (LLMs), Model Context Protocol (MCP), Agentic AI, and other advanced NLP techniques
  • Extensive experience with data pipeline and messaging technologies such as Apache Kafka, Airflow, and cloud data platforms (e.g., Snowflake)
  • Expert-level proficiency in Python, SQL, and other relevant programming languages and tools
  • Proficiency in Amazon Web Services (AWS) and Google Cloud Platform (GCP)
  • Strong understanding of cloud-native technologies and containerization (e.g., Kubernetes, Docker) with experience in managing these systems globally
  • Demonstrated ability to solve complex technical challenges and deliver scalable solutions
  • Excellent communication skills with a collaborative approach to working with global teams and stakeholders
  • Experience working in fast-paced environments, particularly in industries that rely on data-intensive technologies (experience in fintech is highly desirable)

Nice to have

  • Familiarity with public and private equity data and related entity models, enabling smarter features, evaluation sets, and downstream integrations
  • Experience in fintech is highly desirable

What we offer

  • Hybrid work environment (four days in-office each week in most locations)
  • A range of other benefits are also available to enhance flexibility as needs change
  • Tools and resources to engage meaningfully with your global colleagues
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