Tech Lead

AB InBev APAC; Kolkata District; Hybrid; INR 3,500,000 - 6,500,000; Full time

Job summary

AB InBev APAC is seeking an experienced Machine Learning Engineering Tech Lead to provide hands-on technical leadership for enterprise-scale ML, Generative AI and Agentic AI solutions. The role spans data and feature engineering through model integration, deployment, MLOps and production support.

The ideal candidate combines deep ML engineering with strong software engineering, cloud-native architecture and technical leadership capabilities to deliver scalable, secure and cost-efficient AI

Qualifications

• Strong programming skills in Python and SQL.
• Hands-on ML engineering across the ML lifecycle.
• Experience with cloud ML platforms (Azure ML, Databricks).
• Proficiency with CI/CD and DevOps practices.

Responsibilities

• Provide hands-on technical leadership for ML engineering teams.
• Define engineering standards and architectures for ML solutions.
• Oversee end-to-end ML workflows from data to deployment.
• Drive scalability, security and cost efficiency of AI products.

Skills

Python

SQL

PyTorch

TensorFlow

LLMs

MLOps

FastAPI

Kubernetes

Azure ML

Tools

Docker

Kubernetes

Azure ML

Databricks

LangChain

GitHub Actions

MLflow

Job description

Dreaming big is in our DNA. It’s who we are as a company. It’s our culture. It’s our heritage. And more than ever, it’s our future. A future where we’re always looking forward. Always serving up new ways to meet life’s moments. A future where we keep dreaming bigger. We look for people with passion, talent, and curiosity, and provide them with the teammates, resources and opportunities to unleash their full potential. The power we create together - when we combine your strengths with ours - is unstoppable. Are you ready to join a team that dreams as big as you do?

Role Summary

We are seeking an experienced Machine Learning Engineering Tech Lead to provide hands-on technical leadership for enterprise-scale Machine Learning, Generative AI and Agentic AI solutions. The role owns engineering quality across the full AI lifecycle—from data and feature engineering through model/LLM integration, deployment, MLOps, observability, security, scalability and production support. The ideal candidate combines deep ML engineering expertise with strong software engineering, cloud-native architecture and technical leadership capabilities.

Key Responsibilities

ML Engineering Leadership

• Lead and mentor ML Engineers and Data Scientists across multiple AI/ML initiatives.
• Define engineering standards, reusable frameworks, reference architectures and code-quality practices for ML solutions.
• Own technical design and code reviews, production-readiness reviews and complex problem resolution.
• Drive scalability, reliability, maintainability, security, performance and cost efficiency across AI products.

Machine Learning Architecture

• Design end-to-end ML architectures covering data, feature engineering, training, evaluation, registry, deployment, monitoring and retraining.
• Define batch and real-time inference patterns, model serving strategies, feature management, model versioning and rollback mechanisms.
• Establish reproducible experimentation, validation and model lifecycle standards.

Generative AI & LLM Engineering

• Lead enterprise GenAI solution design using LLMs, RAG, embeddings, vector databases, prompt engineering, structured outputs and tool/function calling.
• Design for context-window management, token optimization, semantic/prompt caching, model routing, hybrid search, reranking and grounded response generation.
• Implement LLM evaluation, hallucination mitigation, citations/grounding, guardrails and responsible AI controls.

Agentic AI Engineering

• Design multi-agent workflows, orchestration patterns, tool usage, memory strategies and human-in-the-loop controls.
• Implement retry/failure handling, secure agent-tool boundaries and observability for agent execution.
• Experience with LangGraph, LangChain, CrewAI, Semantic Kernel, AutoGen or similar frameworks is desirable.

RAG Architecture

• Design document ingestion, chunking, metadata, embeddings, vector retrieval, reranking and access-control-aware retrieval pipelines.
• Optimize RAG for retrieval accuracy, answer relevance, latency, cost, security and scalability.

MLOps & Model Lifecycle

• Establish automated training, experiment tracking, model registry, CI/CD, model validation, promotion, deployment, monitoring and retraining pipelines.
• Implement rollback, canary/blue-green deployment and model release governance.

Model Deployment & Cloud Engineering

• Deploy ML/GenAI workloads using Docker, Kubernetes/AKS, APIs, batch inference and event-driven architectures.
• Design autoscaling, GPU/CPU optimization, high availability, fault tolerance and graceful degradation.
• Prefer strong experience with Azure Machine Learning, Azure OpenAI, AKS, ACR, Databricks, Blob/ADLS, Service Bus, Key Vault and Azure Monitor.

Observability, Security & Cost Optimization

• Implement monitoring for model quality, drift, data quality, latency (P50/P95/P99), throughput, errors, GPU/CPU utilization, token consumption and cost.
• Optimize LLM and infrastructure cost using prompt optimization, caching, model routing, batching and autoscaling.
• Ensure PII protection, prompt-injection protection, secure tool execution, RBAC, audit logging, data-residency controls and enterprise security compliance.

Software Engineering Excellence

• Ensure ML products follow clean architecture, modular design, API-first development, automated testing, code review, static analysis and security scanning.
• Promote production-grade engineering beyond notebook-centric experimentation.

Required Technical Skills

• Strong programming skills in Python and SQL; experience with PyTorch, TensorFlow and/or scikit-learn.
• Strong knowledge of FastAPI/REST APIs, microservices, Docker and Kubernetes.
• Hands-on expertise in Machine Learning, Deep Learning, NLP, Generative AI, LLMs, RAG, embeddings, vector databases and prompt engineering.
• Practical knowledge of Agentic AI, LLM evaluation, AI guardrails and responsible AI.
• Good understanding of Spark, data pipelines, distributed processing, Delta Lake/data-lake patterns, feature engineering and data quality.
• Experience with MLflow, Azure ML, Databricks, Kubeflow and/or comparable MLOps platforms.
• Experience with GitHub Actions, Azure DevOps or equivalent CI/CD tooling.