Home
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 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

DomainTechnologies & Tools
ML FrameworksPyTorch, TensorFlow, JAX, Scikit-learn, XGBoost, LightGBM
GenAI PlatformsOpenAI, Anthropic Claude, Google Gemini, Cohere, Amazon Bedrock
Open-Source LLMsLLaMA 2/3, Mistral, Mixtral, Falcon, Phi-3, Gemma, Qwen
RAG & Vector DBsLangChain, LlamaIndex, Pinecone, Weaviate, ChromaDB, pgvector, Qdrant
Agentic AILangGraph, AutoGen, CrewAI, Semantic Kernel, OpenAI Assistants API
Fine-Tuning (PEFT)LoRA, QLoRA, Prefix Tuning, Prompt Tuning, Adapters, RLHF, DPO
MLOpsMLflow, Weights & Biases, DVC, Kubeflow, Airflow, BentoML, Seldon
Cloud & InfraAWS SageMaker, GCP Vertex AI, Azure ML, Docker, Kubernetes
Experiment TrackingMLflow, W&B, Comet ML, Neptune
ProgrammingPython, SQL, Bash, any frontend tech react/ angular; optional: Rust, Go for serving components




View all job openings