Senior AI Engineer

Position: Senior AI Engineer 

Location: Hyderabad 

Years of experience:  08 – 14 years 

Working Pattern: Hybrid 

Job Description Summary: 

Title: AI Engineer ML & AI, AI Engineer – Conversational & Agentic AI 

Core Skills & Experience 

• Senior Data Science Expertise 
  Strong experience applying machine learning, statistical modelling, and experimentation to solve real business problems. 

• Advanced NLP & GenAI Capability 
  Deep theoretical and applied knowledge of NLP, blending traditional NLU approaches with modern LLMs and Generative AI techniques. 

• Conversational & Agentic AI Systems 
  Experience building conversational AI solutions, including agentic workflows, orchestration, and memory‑aware systems. 

• Business Translation & Influence 
  Proven ability to translate complex data science outputs into clear insights for non‑technical audiences, influencing senior decision‑makers. 

• Strong Engineering & Coding Skills 
  Advanced Python and query language, API proficiency, writing modular, maintainable code; fluent with pandas and common DS libraries. 

• Coaching & Technical Leadership 
  Ability to coach other data scientists, review code, and guide teams toward effective, business‑aligned solutions. 

• Modern Software & Delivery Practices 
  Experience working with DevOps, CI/CD, and modern development toolchains to productionise ML and AI solutions. 

• Collaboration & Stakeholder Management 
  Excellent communication and collaboration across engineering, business teams, and third‑party SaaS providers. 

What We Need (Must‑Have) 

• Hands‑on Modelling & Experimentation 
  Practical experience designing experiments and applying complex statistical methods end‑to‑end. 

• Advanced Python & ML Frameworks 
  Strong proficiency in Python and libraries such as NumPy, Pandas, scikit‑learn, PyTorch / TensorFlow. 

• End‑to‑End ML & GenAI Design 
  Strong understanding of feature engineering, model selection, evaluation, optimisation, and GenAI approaches. 

Experience with modern model evaluation and testing approaches for ML and Generative AI, including ML, LLM evaluation, robustness testing, bias and safety checks, and continuous quality monitoring. 

• Agentic AI & Orchestration 
  Experience with RAG, prompt and reasoning design, memory management, and tools such as LangChain / LangGraph. 

• Cloud‑Based AI Deployment 
  Good Familiarity with for deploying microservices, ML and AI workloads. Experience with MLOps tools (e.g. MLflow, Docker, CI/CD pipelines) 

• Business‑Ready Delivery 
  Ability to take complex business requirements and turn them into scalable, production‑ready AI solutions. 

• Strong Communication & Influence 
  Confidence explaining technical outcomes in business terms and influencing non‑technical stakeholders.