Lead AIML

Mancer Consulting Services; Gurugram District; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Mancer Consulting Services is seeking a Lead AI/ML Engineer to drive enterprise-scale AI/ML initiatives. The role collaborates with data scientists, software engineers, and product teams to translate cutting-edge research into production-ready AI solutions.

The ideal candidate excels in NLP, LLMs, information retrieval, and generative AI, capable of delivering end-to-end ML lifecycle from research to deployment in cloud environments.

Qualifications

• The candidate has deep expertise in ML/DL, NLP, LLMs, IR and information extraction.
• Experience taking AI solutions from proof-of-concept to production.
• Strong background in scalable cloud-based AI technologies.

Responsibilities

• Lead end-to-end training and fine-tuning of Large Language Models (LLMs).
• Design and implement GraphRAG pipelines using knowledge graphs for grounding.
• Design, train, and optimize semantic embeddings for document understanding and search.
• Develop semantic retrieval systems with document segmentation and indexing strategies.
• Build and scale distributed training environments for multi-GPU/multi-node setups.
• Apply RL techniques (RLHF, RLAIF, PPO, DPO, GRPO) to improve model alignment.
• Translate business and technical requirements into scalable AI solutions.
• Collaborate with cross-functional teams to deploy AI/ML solutions in production.
• Conduct experiments, benchmarking, and performance characterization of AI models.
• Translate research concepts into practical production-grade solutions.
• Drive innovation in NLP, Generative AI, LLMs, information extraction.

Skills

Machine Learning

Deep Learning

Natural Language Processing

Large Language Models

Information Retrieval

Information Extraction

Generative AI

Job description

Lead AI/ML Engineer About the Role

We are seeking an experienced Lead AI/ML Engineer to drive the design, development, and deployment of advanced Artificial Intelligence and Machine Learning solutions at enterprise scale.

The role will work closely with senior data scientists, machine learning engineers, software engineers, product teams, and subject matter experts to develop innovative AI solutions and translate advanced research into production-ready applications.

The ideal candidate will have deep expertise in Machine Learning, Deep Learning, Natural Language Processing, Large Language Models, Information Retrieval, Information Extraction, and Generative AI, along with a proven track record of taking AI solutions from proof of concept to production.

Job Description

The role will focus on enhancing and developing advanced NLP and AI technologies and extending them into scalable cloud-based applications.

The candidate will be responsible for developing novel machine and deep learning techniques for information extraction, document understanding, semantic search, retrieval, and synthesis.

The role involves the complete AI development lifecycle, including research, experimentation, proof-of-concept development, model training and fine-tuning, performance evaluation, optimization, and production deployment.

Primary Responsibilities

• Lead end-to-end training and fine-tuning of Large Language Models (LLMs) across open-source and closed-source ecosystems.


• Design and implement GraphRAG pipelines, including knowledge graph representation and retrieval for improved contextual grounding.


• Design, train, and optimize semantic and dense vector embeddings for document understanding, search, and retrieval.


• Develop advanced semantic retrieval systems using effective document segmentation, preprocessing, and indexing strategies.


• Build and scale distributed training environments for multi-GPU and multi-node model training.


• Apply reinforcement learning techniques such as RLHF, RLAIF, PPO, DPO, and GRPO to improve model alignment and performance.


• Translate business and technical requirements into scalable AI-driven solutions.


• Collaborate with cross-functional teams to develop and deploy AI/ML solutions into production environments.


• Conduct experimentation, benchmarking, and performance characterization of AI models.


• Translate research concepts and published methodologies into practical proof-of-concepts and production-grade solutions.


• Drive innovation in NLP, Generative AI, LLMs, information extr