Gen AI Engineer

Role\nOverview You\nwill be involved in full lifecycle AI solution delivery \u2013 from research and\nprototyping to scaling and deployment. The ideal candidate combines strong\ntheoretical ML/DL grounding with applied experience in foundation models, LLM\nfine-tuning, vector search, Gen AI application frameworks, and deployment on\nlarge-scale infrastructure. Key\nResponsibilities · Architect, build, and deploy Generative AI solutions\ntailored to business problems. · Fine-tune and customize foundation/LLM models (e.g.,\nGPT, LLaMA, Mistral, Falcon, Claude, Gemma). · Build LLM-powered applications using frameworks such\nas LangChain, Haystack, LlamaIndex. · Develop and manage RAG (Retrieval Augmented\nGeneration) pipelines integrating vector databases (FAISS, Pinecone, Weaviate,\nMilvus, ChromaDB). · Work with cloud AI services such as Azure OpenAI, AWS\nBedrock, SageMaker, GCP Vertex AI. · Implement MLOps pipelines for model\ntraining/monitoring using tools like MLflow, Kubeflow, Weights & Biases,\nDVC. · Leverage Hugging Face ecosystem (Transformers,\nDiffusers, PEFT, Datasets) for model experimentation. · Optimize AI workflows with GPU acceleration and\ninference optimization (e.g., ONNX, TensorRT, DeepSpeed, vLLM). · Design and enforce secure, ethical, and responsible AI\npractices in all deployments. · Collaborate with consultants, data engineers, and\nbusiness analysts to understand client problems and deliver measurable\nsolutions. · Mentor junior engineers; contribute to internal\naccelerators and reusable solution templates. Requirements Required\nQualifications · Education: Bachelor\u2019s/Master\u2019s in Computer Science,\nData Science, AI/ML, or a related field. · Experience: 5+ years in ML/DL, with minimum 2 years in\nGenerative AI solution development. · Expertise with Python and ML/DL libraries (PyTorch,\nTensorFlow, JAX). · Strong knowledge of LLM training/fine-tuning\ntechniques: LoRA, QLoRA, PEFT, instruction tuning. · Proficiency in prompt engineering and evaluation of\nmodel outputs. · Hands-on with vector databases and indexing pipelines\nfor semantic search. · Familiar with containerization and deployment tools\n(Docker, Kubernetes, Helm). · Exposure to CI/CD pipelines for AI solutions and\ncloud-native patterns. Preferred\nSkills · Experience in enterprise AI implementation (chatbots,\ndocument intelligence, knowledge assistants, customer interaction systems). · Exposure to multimodal AI (e.g., CLIP, Stable\nDiffusion, DALL·E, Whisper). · Contributions to open-source or personal projects\nshowcasing Gen AI apps. · Experience in reinforcement learning (RLHF/DPO) for\nmodel alignment. · Working knowledge of streaming and event-driven\narchitectures (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. \u200bBe part of the exciting Growth Story of Thoucentric!