Gen AI ML Engineer

We are excited to present a dynamic opportunity at the forefront of AI and data science for a Data Scientist to join our team. You will translate complex data into actionable insights, design scalable ML/GenAI solutions, and contribute to a cutting-edge product roadmap. This role blends hands-on model development with collaboration across cross-functional teams, enabling impactful outcomes in a fast-paced environment.

What you will do:

Design, develop, and productionize scalable ML/GenAI models and pipelines with a focus on reliability, accuracy, and cost efficiency

Build Retrieval-Augmented Generation (RAG) pipelines and agentic systems using advanced LLMs

Fine-tune, optimize, and deploy transformer-based models (GPT, Llama, Mistral, etc.) for enterprise use cases

Architect multi-agent frameworks (tool-augmented reasoning, orchestration, memory management)

Deliver robust APIs and services for ML/GenAI models with performance monitoring and governance

Collaborate with software engineers and DevOps to scale AI systems across cloud, hybrid, and on-prem environments

Drive automation in model deployment, monitoring, retraining, and drift detection

Ensure security, compliance, and governance while delivering high-value AI solutions

Stay ahead of GenAI and infrastructure innovations, translating them into actionable product enhancements

The skills you bring:

Bachelor s/Master s in Computer Science, Data Science, Statistics, or a related field

Demonstrated experience delivering production-grade ML/GenAI applications

Strong Python (3.x) proficiency with ML/DL frameworks (PyTorch, TensorFlow, Hugging Face)

Hands-on experience with LLMs, fine-tuning methods (LoRA, PEFT, quantization)

Deep expertise in RAG pipelines, embeddings, and vector databases (e.g., Elastic, Pinecone, Milvus)

Practical knowledge of GenAI frameworks (LangChain, LlamaIndex, Haystack, LangGraph, AutoGen, Crew.ai)

Understanding of multi-agent systems, orchestration, and reasoning loops

Familiarity with Model Context Protocol (MCP) for secure GenAI integrations

Experience with cloud-native ML engineering (Azure OpenAI, AWS Bedrock) and container orchestration (Docker/Kubernetes)

Appreciation of Responsible AI practices, including transparency and ethical use of GenAI

Proficiency with MLOps: CI/CD for ML, monitoring, retraining, and scaling