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 advantages 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

📌 senior AI/ML Engineer (Bengaluru)
🏢 Orcapod Consulting Services
📍 Bengaluru