ML & Generative AI DeveloperSenior Individual Contributor | 6–8 Years Experience

Role OverviewWe 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 LearningLevel:Senior Individual ContributorExperience:6–8 YearsEmployment Type:Full-timeLocation:In Office

Key ResponsibilitiesMachine 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 qualified 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

DomainTechnologies &

• ToolsML FrameworksPyTorch, TensorFlow, JAX, Scikit-learn, XGBoost, LightGBMGenAI PlatformsOpenAI, Anthropic Claude, Google Gemini, Cohere, Amazon BedrockOpen-Source LLMsLLaMA 2/3, Mistral, Mixtral, Falcon, Phi-3, Gemma, QwenRAG &

• Vector DBsLangChain, LlamaIndex, Pinecone, Weaviate, ChromaDB, pgvector, QdrantAgentic AILangGraph, AutoGen, CrewAI, Semantic Kernel, OpenAI Assistants APIFine-Tuning (PEFT)LoRA, QLoRA, Prefix Tuning, Prompt Tuning, Adapters, RLHF, DPOMLOpsMLflow, Weights &

• Biases, DVC, Kubeflow, Airflow, BentoML, SeldonCloud &

• InfraAWS SageMaker, GCP Vertex AI, Azure ML, Docker, KubernetesExperiment TrackingMLflow, W&B;, Comet ML, NeptuneProgrammingPython, SQL, Bash, any frontend tech react/ angular; optional: Rust, Go for serving components

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