Key ResponsibilitiesDesign, develop, train, evaluate, and deploy machine learning and deep learning models for business use cases.Develop and productionize Generative AI applications using LLMs and foundation models.Build RAG-based applications using embeddings, vector databases, document processing, retrieval, reranking, and contextual generation.Design and implement Agentic AI systems capable of planning, reasoning, tool usage, task execution, and multi-step decision-making.Develop AI agents using frameworks such as LangChain, LangGraph, LlamaIndex, AutoGen, or equivalent technologies.Integrate LLMs with external APIs, databases, enterprise systems, search engines, and internal tools.Implement prompt engineering, structured outputs, function/tool calling, memory, context management, and agent orchestration.Fine-tune or adapt LLMs using techniques such as LoRA/PEFT, instruction tuning, and supervised fine-tuning, where appropriate.Develop NLP and ML solutions including text classification, information extraction, semantic search, recommendation, forecasting, and anomaly detection.Design experiments and evaluate models using appropriate offline and online evaluation metrics.Implement GenAI evaluation frameworks to measure accuracy, relevance, groundedness, hallucination, latency, and cost.Perform data preprocessing, feature engineering, exploratory data analysis, statistical analysis, and model selection.Build scalable ML/AI pipelines for data ingestion, training, evaluation, deployment, and monitoring.Optimize LLM applications for performance, reliability, latency, scalability, and inference cost.Implement responsible AI practices including guardrails, security, privacy, hallucination mitigation, and prompt-injection protection.Collaborate with software engineers to deploy AI solutions through REST APIs, microservices, and cloud-based infrastructure.Monitor production models and AI applications and continuously improve their performance.Communicate technical findings and AI-driven insights to both technical and non-technical stakeholders.Required SkillsData Science & Machine LearningStrong knowledge of Python, SQL, statistics, probability, and data analysis.Hands-on experience with Scikit-learn, Pandas, NumPy, XGBoost/LightGBM, or equivalent.Experience with supervised and unsupervised machine learning.Understanding of model evaluation, experimentation, feature engineering, and model optimization.Deep Learning & NLPExperience with PyTorch and/or TensorFlow.Strong understanding of NLP, Transformers, embeddings, attention mechanisms, and deep learning architectures.Experience working with transformer-based models such as BERT, T5, Llama, Mistral, or equivalent models.Generative AIHands-on experience developing applications using LLMs and Generative AI.Strong understanding of:Prompt engineeringRAGVector embeddingsVector databasesSemantic searchFunction/tool callingStructured generationLLM evaluationHallucination mitigationExperience with APIs/SDKs from major LLM providers or open-source models.Agentic AIPractical experience building AI agents and multi-step agentic workflows.Understanding of:Agent orchestrationPlanning and reasoningTool callingAgent memoryMulti-agent workflowsWorkflow/state managementHuman-in-the-loop systemsAgent evaluation and observabilityExperience with LangGraph, LangChain, LlamaIndex, AutoGen, CrewAI, or similar frameworks is preferred.Cloud & MLOpsExperience deploying ML/AI solutions using AWS, Azure, or GCP.Understanding of Docker, Git, CI/CD, APIs, and cloud deployment.Familiarity with ML lifecycle tools such as MLflow, Kubeflow, or equivalent.Experience with monitoring, logging, model versioning, and production troubleshooting.Databases & Data EngineeringStrong SQL skills and experience with relational databases.Experience with PostgreSQL, MySQL, MongoDB, or equivalent databases.Familiarity with vector databases such as FAISS, Pinecone, Weaviate, Qdrant, or Chroma.Understanding of data pipelines and large-scale data processingSourced from naukri
