[Software Services] : Sr. AI/ML Developer

ML & Generative AI Developer Senior Individual Contributor  |  6–8 Years Experience Role Overview We are looking for a seasoned ML & Generative AI Developer with 6–8 years of hands-on experience to join our AI/ML team. The ideal candidate will bring deep expertise across the full machine learning lifecycle — from model design and training to production deployment — alongside strong command of modern Generative AI technologies, including LLM fine-tuning, RAG pipelines, and autonomous agentic systems. Department: Artificial Intelligence & Machine Learning Level: Senior Individual Contributor Experience: 6–8 Years Employment Type: Full-time Location: In Office Key Responsibilities Machine Learning Model Development Design, develop, and optimize end-to-end machine learning models for classification, regression, NLP, computer vision, and recommendation tasks. Conduct feature engineering, model selection, hyperparameter tuning, and performance evaluation using industry-standard frameworks. Ensure models meet production-grade accuracy, latency, and scalability requirements. Training Pipelines & Data Infrastructure Architect and implement scalable ML training pipelines with robust data ingestion, preprocessing, augmentation, and validation stages. Design and manage distributed training workflows (single-node and multi-GPU/TPU clusters) using frameworks such as PyTorch, TensorFlow, and JAX. Build automated experiment tracking and reproducibility systems using tools like MLflow, Weights & Biases, or DVC. MLOps & Production Engineering Deploy, monitor, and maintain ML models in production using containerized (Docker, Kubernetes) and cloud-native infrastructure. Establish CI/CD pipelines for model training, evaluation, versioning, and automated re-training triggers. Implement model performance monitoring, drift detection, and alerting systems to ensure sustained model health. Manage model registries and artifacts using platforms such as SageMaker, Vertex AI, Azure ML, or open-source MLOps stacks. Generative AI Development Build and deploy production-grade Generative AI applications using leading platforms and open-source models. Proprietary: OpenAI GPT-4/o, Anthropic Claude, Google Gemini, Amazon Titan, Cohere Open-source: Meta LLaMA 2/3, Mistral, Falcon, Mixtral, Phi-3, Gemma Design prompting strategies including zero-shot, few-shot, chain-of-thought (CoT), and structured output prompting for diverse task types. Evaluate LLM outputs for hallucination, toxicity, relevance, and faithfulness using automated and human-in-the-loop evaluation frameworks. RAG, Agentic AI & Fine-Tuning Design and implement Retrieval-Augmented Generation (RAG) pipelines with semantic chunking, hybrid search (dense + sparse), reranking, and document parsing strategies. Build and orchestrate Agentic AI systems with memory, planning, tool use, and multi-agent collaboration using LangChain, LangGraph, AutoGen, CrewAI, or similar frameworks. Apply parameter-efficient fine-tuning (PEFT) techniques, particularly Low-Rank Adaptation (LoRA) and QLoRA, for domain adaptation and instruction tuning of large language models. Implement Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) pipelines where applicable. Required Qualifications 6–8 years of professional experience in machine learning and AI development. Strong proficiency in Python and ML libraries: NumPy, Pandas, Scikit-learn, PyTorch, and/or TensorFlow. Proven experience designing and training ML models across supervised, unsupervised, and self-supervised learning paradigms. Hands-on experience building and maintaining ML training and inference pipelines at scale. Solid MLOps experience: containerization, orchestration, CI/CD, model monitoring, and cloud deployment (AWS, GCP, or Azure). Demonstrated experience with at least two major Generative AI platforms (proprietary or open-source). Deep understanding of Transformer architecture and large language model internals. Practical experience implementing RAG systems with vector databases (Pinecone, Weaviate, ChromaDB, Qdrant, pgvector, Milvus etc.). Hands-on experience with Agentic AI frameworks and multi-step reasoning pipelines. Working knowledge of Low-Rank Adaptation (LoRA/QLoRA) and other PEFT fine-tuning methods. Strong understanding of evaluation frameworks for both discriminative and generative models. Preferred Qualifications Experience with multimodal models (vision-language, text-to-image, speech-to-text). Familiarity with model quantization techniques (GPTQ, AWQ, bitsandbytes) for efficient inference. Exposure to graph neural networks, time-series forecasting, or reinforcement learning. Contributions to open-source ML or GenAI projects. Experience with advanced vector search strategies: ColBERT, hybrid BM25 + ANN, reranking models. Knowledge of responsible AI principles, bias mitigation, and model safety evaluation. Publications or patents in machine learning or AI domains. Technical Skills Matrix Domain Technologies & Tools ML Frameworks PyTorch, TensorFlow, JAX, Scikit-learn, XGBoost, LightGBM GenAI Platforms OpenAI, Anthropic Claude, Google Gemini, Cohere, Amazon Bedrock Open-Source LLMs LLaMA 2/3, Mistral, Mixtral, Falcon, Phi-3, Gemma, Qwen RAG & Vector DBs LangChain, LlamaIndex, Pinecone, Weaviate, ChromaDB, pgvector, Qdrant Agentic AI LangGraph, AutoGen, CrewAI, Semantic Kernel, OpenAI Assistants API Fine-Tuning (PEFT) LoRA, QLoRA, Prefix Tuning, Prompt Tuning, Adapters, RLHF, DPO MLOps MLflow, Weights & Biases, DVC, Kubeflow, Airflow, BentoML, Seldon Cloud & Infra AWS SageMaker, GCP Vertex AI, Azure ML, Docker, Kubernetes Experiment Tracking MLflow, W&B, Comet ML, Neptune Programming Python, SQL, Bash, any frontend tech react/ angular; optional: Rust, Go for serving components