DeepWork Portfolio Careers

Lead AI Engineer

Location: Hyderabad, India – 5 days a week onsite requirement

Experience: 6–10 Years

Company: Circuitry.ai

About Circuitry.ai

Circuitry.ai is an Enterprise AI SaaS company building advanced AI systems for the US SLM sector.

We specialize in:

• Production-grade RAG architectures
• Fine-tuned LLM systems
• Knowledge Graph–driven AI
• Agentic AI frameworks
• Trust-centric AI systems with explainability and evidence

Our focus is solving the “Trust in AI” problem for enterprises by combining LLMs, Graph Retrieval, structured data systems, and advanced evaluation pipelines.

We are building AI systems that go beyond demos — real deployments, real customers, real scale.

Role Overview

We are looking for a Senior Lead AI Engineer who has evolved from a Data Scientist to a Technical Leader, with deep hands-on ML expertise and real production experience.

This role is 85% practical, 15% theoretical.

What You Will Do

• Lead a team of Data Scientists and ML Engineers (minimum 3 members)
• Architect and deploy production-grade ML and GenAI systems
• Design scalable AI pipelines for enterprise use cases
• Apply latest research to real-world production problems
• Work on advanced GenAI systems including:
• RAG architectures
• Fine-tuned LLMs
• Embedding systems
• Knowledge Graph–based retrieval
• Graph Databases (Neo4j or similar)
• Hybrid Graph + Vector retrieval pipelines
• Build evaluation pipelines for LLMs and ML models
• Handle distributed training and large-scale data pipelines
• Drive technical design discussions and AI roadmap decisions
• Mentor engineers and enforce strong engineering discipline

Requirements

Experience

• 6–10 years in Data Science / Machine Learning
• Seeking someone who previously worked as a Hands-on Data Scientist that moved into a Management/Team Lead role. Ideally looking for someone that has managed at least 3 Data Scientists and ML Engineers

Production Experience (Critical)

• Successfully deployed ML or AI models into production
• Must have at least one GenAI project (RAG / LLM / Fine-tuning) in production

Should be able to deeply explain:

• Architecture
• Scaling decisions
• Evaluation approach
• Failure cases
• Trade-offs

Technical Depth:

Strong understanding of:

• ML algorithms
• Model training & optimization
• Model evaluation techniques
• Distributed training
• Data engineering pipelines

Hands-on experience with:

• GenAI / LLM systems
• RAG frameworks
• Embedding models
• Fine-tuning strategies
• Graph Databases
• Graph-based retrieval systems
• Understanding of LLM evaluation and benchmarking

Nice to have Skills:

• Experience in ML model deployment (MLOps, CI/CD, infra)
• Experience with AWS/Azure
• Experience with manufacturing domain datasets
• Exposure to agentic AI systems

Ideal Candidate Profile:

You are someone who:

• Can discuss transformer architecture but also optimize inference latency
• Has read research papers and implemented improvements
• Has dealt with real production failures
• Understands scaling constraints and cost trade-offs
• Can mentor juniors while being hands-on
• Thinks in systems, not just models