senior AI/ML Engineer

Orcapod Consulting Services; Bengaluru; On-site; INR 3,500,000 - 6,000,000; Full time

Job summary

Orcapod Consulting Services seeks a senior AI/ML leader to develop end-to-end LLM training and fine-tuning across open-source and closed systems. You will architect GraphRAG pipelines with knowledge graphs and design semantic embeddings for document understanding and retrieval.

You will drive semantic search, vector databases, and distributed multi-GPU training while applying RLHF/RLAIF to align models with business goals. Collaboration with cross-functional teams is essential.

Qualifications

• Advanced degree in CS/ML or related field.
• 10+ years in applied AI/ML with production-grade models.
• Experience with hybrid NLP combining symbolic and ML.
• Strong PyTorch experience.
• Deep knowledge of transformers and LLMs.
• Proficiency in Python, SQL and data analysis.
• Experience with EDA and document processing.
• Hands-on LLM training and fine-tuning (GPT, LLaMA, Mistral, Qwen).
• Experience with GraphRAG, knowledge graphs, and vector DBs.
• Familiarity with vector embeddings and semantic search.
• Experience with distributed multi-GPU/multi-node training.

Responsibilities

• Develop end-to-end training and fine-tuning of LLMs across ecosystems.
• Architect GraphRAG pipelines with knowledge graphs and retrieval.
• Design and optimize semantic and dense embeddings for docs.
• Build semantic retrieval with document segmentation and indexing.
• Scale distributed training with NCCL and InfiniBand.
• Apply RL techniques (RLHF, RLAIF) to align with goals.
• Collaborate with teams to deploy AI solutions in production.
• Ensure compliance with company policies and directives.

Skills

End-to-end training

LLMs

Hybrid NLP

PyTorch

Transformer models

Python

SQL

EDA

Vector embeddings

Semantic search

GraphRAG

Knowledge graphs

OCR

Model fine-tuning

Education

Graduate degree or equivalent experience

PhD or Masters in CS/ML

Tools

Qwen

LLaMA

Mistral

OpenAI

Gemini

Anthropic

FAISS

Weaviate

Milvus

NCCL

InfiniBand

Azure ML

AWS

GPT

RLAIF

Job description

Role & responsibilities

• Develop end-to-end training and fine-tuning of Large Language Models (LLMs), including both open-source (e.g., Qwen, LLaMA, Mistral) and closed-source (e.g., OpenAI, Gemini, Anthropic) ecosystems
• Architect and implement GraphRAG pipelines, including knowledge graph representation and retrieval for enhanced contextual grounding
• Design, train, and optimize semantic and dense vector embeddings for document understanding, search, and retrieval
• Develop semantic retrieval systems with advanced document segmentation and indexing strategies
• Build and scale distributed training environments using NCCL and InfiniBand for multi-GPU and multi-node training
• Apply reinforcement learning techniques (e.g., RLHF, RLAIF) to align model behavior with human preferences and domain-specific goals
• Collaborate with cross-functional teams to translate business needs into AI-driven solutions and deploy them in production environments
• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

Required Qualifications:

• Graduate degree or equivalent experience
• PhD or Masters degree in computer science, Machine Learning, or related field
• 10+ years of experience in applied AI/ML with statistics, with a Solid track record of delivering production-grade models
• Experience with Hybrid NLP solutions that combine symbolic and machine learning approaches
• Experience with PyTorch
• Deep knowledge and extensive experience with Machine/Deep Learning frameworks including transformer architectures, state space models, large language models, and agentic approaches
• Knowledge of algorithms and techniques within a computational domain with emphasis on text processing
• Proven deep expertise in: NLP, Fundamental machine learning, deep learning, transformer, state space-based architecture Azure ML and/or AWS
• Proven solid in Python coding, SQL and database queries, data preparation, and analysis
• Exploratory Data Analysis (EDA)
• LLM training and fine-tuning (e.g., GPT, LLaMA, Mistral, Qwen)
• Graph-based retrieval systems (GraphRAG, knowledge graphs)
• Embedding models (e.g., BGE, E5, SimCSE)
• Semantic search and vector databases (e.g., FAISS, Weaviate, Milvus)
• Document segmentation and preprocessing (OCR, layout parsing)
• Model fusion and ensemble techniques (stacking, boosting, gating)
• Optimization algorithms (Bayesian, Particle Swarm, Genetic Algorithms)
• Reinforcement learning (e.g., RLHF, PPO, DPO, GRPO), Supervised Fine Tuning (SFT), LoRA, QLoRA, axolotl
• Prompt optimization framework (AutoPrompt, GreaterPrompt, DSPy), GEPA