Lead - Artificial Intelligence/Machine Learning

Job Description :

We are seeking an experienced AI/ML Lead with strong expertise in Traditional Machine Learning, Deep Learning, Generative AI (LLMs), and Harness Engineering. The selected candidate will be responsible for designing, developing, and scaling robust AI solutions, while contributing to architectural decisions and providing technical leadership.

Key Responsibilities :

- Design and implement end-to-end AI/ML and Generative AI solutions.

- Develop and optimize RAG pipelines, LLM-based applications, and Agentic AI systems.

- Own the complete ML lifecycle, including development, evaluation, deployment, monitoring, drift detection, and retraining.

- Build and maintain ML pipelines, MLOps frameworks, and Harness Engineering practices for experimentation and benchmarking.

- Establish evaluation frameworks, observability, and performance monitoring for ML and GenAI systems.

- Collaborate with cross-functional teams across engineering, product, and business units.

- Contribute to pre-sales activities, including solution architecture, proposals, and effort estimation.

- Provide technical leadership through mentoring, code reviews, and best practice implementation.

Required Skills & Expertise :

- Strong proficiency in Python and applied Statistics.

- Expertise in Traditional Machine Learning, including model selection, feature engineering, and evaluation metrics (Precision, Recall, AUC).

- Solid understanding of Deep Learning architectures, including Transformers.

- Hands-on experience with PyTorch, TensorFlow, and scikit-learn.

- Proven experience with Large Language Models (LLMs) (public and private).

- Hands-on experience in building RAG-based applications.

- Strong understanding of embeddings, chunking, and prompt engineering.

- Experience in LLM evaluation, hallucination mitigation, and observability.

- Demonstrated hands-on experience in building Agentic AI systems.

- Experience building scalable Agentic AI applications in production environments.

- Experience with fine-tuning techniques (LoRA, QLoRA).

- Experience in developing evaluation harnesses for ML and LLM systems.

- Strong understanding of :

1. Experiment tracking and reproducibility.

2. Automated benchmarking and testing frameworks.

3. Model and prompt versioning, and comparative evaluation.

- Exposure to end-to-end MLOps practices, including CI/CD for ML systems.

- Experience with LangChain, LangGraph, LlamaIndex, CrewAI, or similar frameworks.

- Working experience of vector databases such as Pinecone, OpenSearch, or FAISS.

- Experience with AWS AI services, including SageMaker and Bedrock (Guardrails, Agents, Knowledge Base).

- Familiarity with Docker, REST APIs, and CI/CD pipelines.

(ref:hirist.tech)