ML Lead

Role Overview

We are looking for an ML Lead to build and run our machine learning platform and to deliver ML and agentic AI solutions for business clients. You will be a hands-on technical leader who owns the full lifecycle, from design and build through production support. You will coach an offshore engineering team and act as the primary technical contact for client stakeholders.

Key Responsibilities

ML Platform Engineering

Design, build, and evolve a scalable, secure ML platform on AWS and Databricks, covering feature engineering, training, experiment tracking, model registry, deployment, and monitoring.

Establish MLOps standards: CI/CD for models and pipelines, infrastructure as code, automated testing, model versioning, and reproducibility.

Implement governance with Unity Catalog, including access control, lineage, and auditability.

Optimize platform cost, performance, and reliability (cluster policies, autoscaling, FinOps practices).

ML and Agentic AI Solutions

Lead the design and delivery of ML use cases such as forecasting, classification, recommendation, and personalization.

Architect and deliver agentic AI solutions: LLM-based agents, RAG pipelines, tool and function calling, multi-step orchestration, and evaluation frameworks.

Define guardrails for LLM applications, including prompt management, hallucination and safety evaluation, observability, and cost controls.

Evaluate new frameworks and models, and make build-versus-buy recommendations.

Delivery and Support Ownership

Own end-to-end product delivery: roadmap, estimation, sprint planning, release management, and quality.

Own production support: incident response, root cause analysis, SLAs, runbooks, on-call rotation, and model performance and drift monitoring.

Balance feature delivery against operational stability and technical debt.

Team Leadership and Offshore Coaching

Lead, mentor, and upskill an offshore team of ML engineers and data engineers.

Set coding, review, and documentation standards, and run design and code reviews.

Provide clear task breakdowns and ensure effective handoffs across time zones.

Build team capability in MLOps, LLM engineering, and platform best practices.

Client and Stakeholder Management

Act as the technical lead with clients: gather requirements, translate business problems into ML solutions, and manage expectations.

Present architecture, progress, risks, and outcomes to technical and executive audiences.

Partner with product, data engineering, security, and architecture teams.

Required Qualifications

8+ years in data science, ML engineering, or data engineering, with 3+ years leading teams or technical delivery.

Strong hands-on experience with Databricks (Spark, Delta Lake, MLflow, Unity Catalog, Workflows, Model Serving, Feature Store).

Strong hands-on experience with AWS (S3, IAM, VPC, SageMaker, Lambda, ECS/EKS, Bedrock, CloudWatch, Glue).

Proven experience building and operating production ML systems, not only prototypes.

Experience delivering LLM and agentic applications (for example LangChain, LangGraph, LlamaIndex, Databricks Mosaic AI Agent Framework, or Bedrock Agents), including vector search and RAG.

Proficiency in Python and SQL; solid software engineering practices (Git, testing, CI/CD, IaC such as Terraform).

Experience leading distributed or offshore teams and coaching engineers.

Strong client-facing skills, with the ability to explain complex topics to non-technical stakeholders.

Experience with production support models, including SLAs, incident management, and monitoring.

Preferred Qualifications

Databricks or AWS certifications (ML Professional, Solutions Architect, etc.).

Experience with model monitoring and observability tools (for example Evidently or Lakehouse Monitoring) and LLM evaluation tooling.

Knowledge of responsible AI, data privacy, and compliance requirements.

Background in consulting or services delivery with multiple client engagements.

Familiarity with Agile delivery and FinOps for AI workloads.