Generative AI Lead Engineer

Role Overview

We are seeking a Lead Gen AI Engineer with proven experience in building and deploying Generative AI and scaled ML systems. The role involves end-to-end ownership of AI solutions — from model selection, fine-tuning, and pipeline design to deployment on cloud ML platforms (AWS Sage Maker).

The candidate should be able to architect multi-agent frameworks and MCP pipelines, integrate them into enterprise systems, and drive scalable, production-grade AI solutions. In addition to strong engineering skills, this role requires mentoring junior engineers, leading PoCs, and collaborating with product and research teams to shape the GenAI roadmap.

Key Responsibilities

Solution Architecture & Design

Architect and implement enterprise-grade generative AI pipelines (LLMs, RAG systems, diffusion models).

Design and optimize multi-component pipelines (MCPs) and agentic frameworks for workflow automation.

Make architectural trade-offs for scalability, latency, and cost.

Model Development & Scaling

Lead development of traditional ML and statistical models, integrating them with GenAI systems.

Fine-tune LLMs, build RAG pipelines with vector databases (FAISS, Pinecone, PgVector), and optimize inference.

Scale training and deployment on AWS sage Maker using distributed training and monitoring techniques.

Engineering & MLOps

Drive CI / CD for ML using GitLab pipelines, ensuring reproducibility and automation across model lifecycle.

Implement observability, monitoring, and governance frameworks for deployed AI models.

Collaborate with DevOps and data engineering teams to integrate AI services into production environments.

Leadership & Collaboration

Mentor junior Gen AI engineers, review code, and establish best practices.

Work with cross-functional teams (product, research, data engineering) to align AI solutions with business objectives.

Lead PoCs, pilots, and research initiatives to evaluate new frameworks and approaches.

Required Skills :

Experience with enterprise AI / GenAI deployments in production environments.

Familiarity with Lang Chain, LlamaIndex, orchestration frameworks.

Exposure to distributed systems, GPU optimization, and cost-aware model scaling.

Programming & Modeling : Advanced proficiency in Python, ML / DL frameworks (PyTorch, TensorFlow, Hugging Face, scikit-learn).

Generative AI Expertise : Hands-on with LLMs, transformers, diffusion models, and RAG architectures.

Agentic & MCP : Strong experience in agent-based frameworks and multi-component pipelines.

Scaling AI : Practical knowledge of parallel / distributed training, optimization, and scaled inference.

Cloud ML : Deep expertise with AWS Sage Maker (training, hyperparameter tuning, endpoints, monitoring).

MLOps : Strong exposure to CI / CD, model lifecycle management, and monitoring tools (GitLab, ML flow, Kube flow).

System Thinking : Ability to design solutions considering throughput, latency, fault tolerance, and observability.