Job Title : AI/ML Team Lead (India)

Location : India (Remote/Hybrid)

Work Hours : Must support minimum 4 hours overlap with PST

Experience Level : Tech Lead

Team Size : Lead and manage ~5 AI/ML Engineers

Employment Type : Full-time

Role Overview :

We are seeking an AI/ML Team Lead based in India to lead a team of AI engineers delivering high-impact, production-grade AI solutions. This role requires a strong balance of technical leadership, hands-on AI/ML expertise, and stakeholder collaboration.

You will be responsible for translating business and product requirements into clear technical plans, mentoring junior engineers, and remaining hands-on with coding and architecture when needed.

Key Responsibilities :

Team Leadership & Mentorship :

- Lead, manage, and mentor a team of ~5 AI/ML engineers.

- Provide technical mentorship to junior and mid-level developers through design reviews, code reviews, and pair programming.

- Foster strong engineering practices, knowledge sharing, and continuous learning within the team.

Stakeholder Collaboration & Planning :

- Partner with product managers, business leaders, and engineering stakeholders to understand requirements and constraints.

- Translate stakeholder needs into actionable technical tasks, system designs, and delivery plans.

- Break down complex AI initiatives into milestones, sprint plans, and measurable outcomes.

Hands-On Technical Contribution :

- Remain hands-on with coding when needed (prototyping, complex modeling, debugging, performance optimization), especially for :

1. Prototyping LLM and agent-based solutions

2. Implementing complex reasoning, memory, and orchestration logic

3. Debugging performance, latency, and reliability issues

- Review and contribute to production-quality code in Python and ML frameworks.

- Guide architecture decisions across data pipelines, model training, inference, and monitoring.

Generative AI & Agentic System Ownership :

- Design, build, and review Generative AI systems, including :

1. LLM-based applications

2. Retrieval-Augmented Generation (RAG)

3. Tool-using and multi-step agentic workflows

4. Planning, reflection, and memory mechanisms

- Define strategies for :

1. Prompt engineering and prompt versioning

2. Fine-tuning vs. RAG vs. hybrid approaches

3. Model evaluation, guardrails, and hallucination mitigation

- Drive adoption of best practices for safe, reliable, and scalable GenAI systems.

AI Foundations & Quality :

- Apply a strong theoretical AI/ML foundation to guide design decisions and tradeoffs.

- Ensure rigorous approaches to :

1. Model evaluation and benchmarking

2. Bias/variance trade-offs

3. Generalization and robustness

4. Responsible AI and system safety

- Establish observability, monitoring, and continuous improvement loops for GenAI systems.

Cross-Time-Zone Collaboration :

- Collaborate effectively with global teams, maintaining at least 4 hours of daily overlap with US PST.

- Communicate clearly and proactively across time zones.

Required Qualifications :

Must-Have :

- 7+ years of experience in AI/ML, with recent hands-on work in Generative AI.

- 2+ years leading or mentoring AI/ML engineers.

- Strong experience building LLM-powered applications in production.

- Deep understanding of Generative AI concepts, including :

1. Large Language Models (LLMs)

2. Prompt engineering and prompt optimization

3. Retrieval-Augmented Generation (RAG)

4. Embeddings and vector search

5. Tool calling and function execution

6. Agentic architectures (planning, reasoning, memory, orchestration)

- Strong theoretical AI/ML background, including :

1. Supervised and unsupervised learning

2. Optimization and loss functions

3. Probability and statistics

4. Model evaluation and experiment design

- Proven ability to translate business requirements into technical execution plans.

- Strong hands-on coding skills in Python.

- Experience deploying AI systems to production environments.

Nice-to-Have :

- Experience with multi-agent systems or autonomous workflows.

- Familiarity with GenAI frameworks (e.g., Langgraph, LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen).

- Experience with LLM fine-tuning, adapters (LoRA), or model compression.

- MLOps experience for GenAI (CI/CD, monitoring, evaluation pipelines).

- Cloud experience (Azure, AWS, or GCP).

- Experience designing AI guardrails, safety layers, and evaluation harnesses.

What Success Looks Like :

- Successful delivery of production-grade GenAI and agentic systems aligned with business goals.

- A technically strong, well-mentored AI team capable of independent execution.

- Clear, repeatable patterns for building and scaling GenAI solutions.

- High trust and confidence from stakeholders through predictable execution.

Why Join Us :

- Lead cutting-edge Generative AI and agentic system initiatives with real business impact.

- Balance leadership with meaningful hands-on technical work.

- Influence AI architecture, standards, and best practices.

- Work with global stakeholders while building a strong GenAI engineering culture.