Senior Data Scientist

Job description

Job Description SummaryWe are seeking a highly skilled and innovative AI Engineer with expertise in both traditional Artificial Intelligence and emerging Generative AI technologies. In this role, you will be responsible for designing, developing, and deploying intelligent systems that leverage machine learning, deep learning, and generative models to solve complex problems. You will work across the AI lifecycle—from data engineering and model development to deployment and monitoring—while also exploring GenAI applications, Agentic AI and developing agentic platforms. The ideal candidate combines strong technical acumen with a passion for experimentation, rapid prototyping, and delivering scalable AI solutions in real-world environments. Job DescriptionJob DescriptionRoles and Responsibilities In this role, you will: Develop and fine-tune Generative AI models (e.g., LLMs, diffusion models). Design and implement machine learning models for classification, regression, clustering, and recommendation tasks. Build and maintain scalable AI pipelines for data ingestion, training, evaluation, and deployment. Collaborate with cross-functional teams to understand business needs and translate them into AI solutions. Ensure model performance, fairness, and explainability through rigorous testing and validation. Deploy models to production using MLOps tools and monitor their performance over time. Stay current with the latest research and trends in AI/ML and GenAI and evaluate their applicability to business problems. Document models, experiments, and workflows for reproducibility and knowledge sharing. Technical Skill Set Cloud & Infrastructure (AWS) Amazon SageMaker – Model training, tuning, deployment, and MLOps. Amazon Bedrock – Serverless GenAI model access and orchestration. SageMaker JumpStart – pre-trained models and GenAI templates. Prompt engineering and fine-tuning of LLMs using SageMaker or Bedrock. Programming & Scripting Python – Primary language for AI/ML development, data processing, and automation. Education QualificationBachelor’s degree in engineering with minimum four years of experience in relevant technologies. Desired CharacteristicsTechnical Expertise:1. GenAI Platforms & Models Familiarity with LLMs: like Claude (Anthropic), LLaMA (Meta), Gemini (Google), Mistral, Falcon Experience with APIs: Amazon Bedrock. Understanding of model types: encoder-decoder, decoder-only, diffusion models Design, develop, and deploy agent-based AI systems that exhibit autonomous decision-making. Integrate Generative AI (LLMs, diffusion models) into real-world applications. 2. Prompt Engineering & Fine-Tuning Prompt design for zero-shot, few-shot, and chain-of-thought reasoning Fine-tuning and parameter-efficient tuning (LoRA, PEFT) Retrieval-Augmented Generation (RAG) design and implementation 3. System Integration & Architecture Event-driven and serverless architectures (e.g., AWS Lambda, EventBridge) 4. Development Frameworks LangChain, LlamaIndex. Vector databases: FAISS, Pinecone, Weaviate, Amazon OpenSearch Langgraph, Langchain 5. Cloud & DevOps AWS (Bedrock, SageMaker, Lambda, S3), Azure (OpenAI, Functions), GCP (Vertex AI) CI/CD pipelines for GenAI workflows 6. Security & Compliance Data privacy and governance (GDPR, HIPAA) Model safety: content filtering, moderation, hallucination control 7. Monitoring & Optimization Model performance tracking (latency, cost, accuracy) Logging and observability (CloudWatch, Prometheus, Grafana) Cost optimization strategies for GenAI inference 8. Collaboration & Business Alignment Working with product, legal, and compliance teams Translating business requirements into GenAI use cases Creating PoCs and scaling to production Additional InformationRelocation Assistance Provided: No