Overview

We are looking for an experienced AI/ML Lead with deep expertise in Traditional Machine Learning, Deep Learning, Generative AI (LLMs), Agentic AI, Model Context Protocol (MCP), MLOps, and AI Evaluation Harness Engineering.

Qualifications

• Lead the architecture, design, and implementation of enterprise-grade AI/ML and Generative AI solutions from concept to production.
• Define end-to-end AI solution architecture, including data pipelines, model orchestration, infrastructure, security, scalability, and deployment strategies.
• Architect and build scalable RAG applications, LLM-powered platforms, Agentic AI systems, and MCP-enabled AI solutions integrated with enterprise applications, APIs, and external tools.
• Design robust multi-agent architectures and intelligent workflows leveraging Model Context Protocol (MCP) for standardized communication across AI agents and enterprise systems.
• Own the complete AI/ML lifecycle, including model development, validation, deployment, monitoring, drift detection, optimization, and continuous improvement.
• Lead the implementation of MLOps practices, AI evaluation frameworks, Harness Engineering, automated benchmarking, observability, and governance.
• Establish AI architecture standards, reusable frameworks, best practices, and design patterns across engineering teams.
• Collaborate with Product, Engineering, Data, and Business teams to translate business challenges into scalable AI architectures and technical solutions.
• Provide technical leadership through architecture reviews, design discussions, code reviews, and mentoring of AI/ML engineers.
• Drive technical decision-making for model selection, infrastructure, cloud architecture, cost optimization, performance tuning, and AI platform scalability.
• Support client engagements and pre-sales initiatives by creating solution architectures, technical proposals, effort estimations, and participating in technical discussions.
• Strong proficiency in Python, Statistics, Algorithms, and Data Structures.
• Extensive experience in Traditional Machine Learning, including feature engineering, model selection, hyperparameter tuning, and evaluation metrics (Precision, Recall, F1 Score, ROC-AUC).
• Strong understanding of Deep Learning architectures, including CNNs, RNNs, Transformers, and modern foundation models.
• Hands-on experience with PyTorch, TensorFlow, and Scikit-learn.
• Proven expertise in designing, deploying, and optimizing Large Language Model (LLM) applications using both commercial and open-source models.
• Strong experience building scalable Retrieval-Augmented Generation (RAG) applications.
• Deep understanding of embeddings, semantic search, vector indexing, chunking strategies, prompt engineering, and context optimization.
• Experience implementing LLM evaluation, hallucination mitigation, AI observability, safety guardrails, and responsible AI practices.
• Hands-on experience architecting and deploying production-grade Agentic AI applications and autonomous AI workflows.
• Strong expertise in implementing Model Context Protocol (MCP) for integrating AI agents with enterprise systems, APIs, tools, and multi-agent environments.
• Experience with fine-tuning techniques including LoRA and QLoRA.
• Expertise in developing AI evaluation harnesses, automated testing, benchmarking frameworks, and performance validation pipelines.
• Strong understanding of:
• Experiment tracking and reproducibility
• Model and prompt versioning
• Automated benchmarking and comparative evaluation
• CI/CD pipelines and end-to-end MLOps practices
• Experience with AI orchestration frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or similar.
• Hands-on experience with vector databases such as Pinecone, OpenSearch, FAISS, ChromaDB, or Weaviate.