Role Overview You will be involved in full lifecycle AI solution delivery – from research and prototyping to scaling and deployment. The ideal candidate combines strong theoretical ML/DL grounding with applied experience in foundation models, LLM fine-tuning, vector search, Gen AI application frameworks, and deployment on large-scale infrastructure. Key Responsibilities · Architect, build, and deploy Generative AI solutions tailored to business problems. · Fine-tune and customize foundation/LLM models (e.g., GPT, LLaMA, Mistral, Falcon, Claude, Gemma). · Build LLM-powered applications using frameworks such as LangChain, Haystack, LlamaIndex. · Develop and manage RAG (Retrieval Augmented Generation) pipelines integrating vector databases (FAISS, Pinecone, Weaviate, Milvus, ChromaDB). · Work with cloud AI services such as Azure OpenAI, AWS Bedrock, SageMaker, GCP Vertex AI. · Implement MLOps pipelines for model training/monitoring using tools like MLflow, Kubeflow, Weights & Biases, DVC. · Leverage Hugging Face ecosystem (Transformers, Diffusers, PEFT, Datasets) for model experimentation. · Optimize AI workflows with GPU acceleration and inference optimization (e.g., ONNX, TensorRT, DeepSpeed, vLLM). · Design and enforce secure, ethical, and responsible AI practices in all deployments. · Collaborate with consultants, data engineers, and business analysts to understand client problems and deliver measurable solutions. · Mentor junior engineers; contribute to internal accelerators and reusable solution templates. Requirements Required Qualifications · Education: Bachelor’s/Master’s in Computer Science, Data Science, AI/ML, or a related field. · Experience: 5+ years in ML/DL, with minimum 2 years in Generative AI solution development. · Expertise with Python and ML/DL libraries (PyTorch, TensorFlow, JAX). · Strong knowledge of LLM training/fine-tuning techniques: LoRA, QLoRA, PEFT, instruction tuning. · Proficiency in prompt engineering and evaluation of model outputs. · Hands-on with vector databases and indexing pipelines for semantic search. · Familiar with containerization and deployment tools (Docker, Kubernetes, Helm). · Exposure to CI/CD pipelines for AI solutions and cloud-native patterns. Preferred Skills · Experience in enterprise AI implementation (chatbots, document intelligence, knowledge assistants, customer interaction systems). · Exposure to multimodal AI (e.g., CLIP, Stable Diffusion, DALL·E, Whisper). · Contributions to open-source or personal projects showcasing Gen AI apps. · Experience in reinforcement learning (RLHF/DPO) for model alignment. · Working knowledge of streaming and event-driven architectures (Kafka, Flink) for real-time AI applications. Benefits What a Consulting role at Thoucentric will offer you? Opportunity to define your career path and not as enforced by a manager A great consulting environment with a chance to work with Fortune 500 companies and startups alike. A dynamic but relaxed and supportive working environment that encourages personal development. Be part of One Extended Family. We bond beyond work - sports, get-togethers, common interests etc. Work in a very enriching environment with Open Culture, Flat Organization and Excellent Peer Group. Be part of the exciting Growth Story of Thoucentric!
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