Position-AI Engineer - Senior/Lead/Principal
Exp-3- 5yrs Senior; 5 - 8yrs Lead; 8+yrs Principal
Work Mode: Hybrid
Work Location: Whitefield ITPL
Position Summary
We are seeking an AI Engineer with 3–12 years of experience to design, build, and operationalize enterprise‑scale AI and Generative AI solutions in a fast‑moving business environment. This role requires strong hands‑on engineering capability, combined with architectural judgment and end‑to‑end ownership of AI systems.
The role focuses on production‑grade AI delivery, Generative AI platforms, Agentic AI systems, and MLOps/LLMOps practices, ensuring solutions are scalable, secure, cost‑effective, and well‑governed. A key expectation is the ability to rapidly convert business problems into AI‑enabled workflows, co‑pilot experiences, and intelligent automation using both code‑driven and low‑code/no‑code approaches.
Key Responsibilities
AI & Generative AI Engineering
- Design, develop, and deploy AI and Generative AI applications for enterprise use cases.
- Build and optimize LLM‑based solutions, including prompt engineering, context design, RAG pipelines, vector search, embeddings, and evaluation frameworks.
- Integrate foundation models (OpenAI, Anthropic, Gemini, and open‑source LLMs) into production systems with appropriate reliability, safety, and cost controls.
Agentic AI & Workflow Automation
- Design and implement agentic AI workflows, including single‑agent and multi‑agent systems, to solve complex business problems.
- Build autonomous and semi‑autonomous agent workflows with tool/function calling, orchestration logic, state management, and human‑in‑the‑loop controls.
- Create AI co‑work and co‑pilot solutions that augment business users and teams, enabling faster decision‑making and execution.
- Leverage low‑code / no‑code and orchestration platforms (e.g., Claude‑based workflows, n8n, similar automation tools) to rapidly assemble, iterate, and deploy AI‑powered workflows in high‑velocity environments.
MLOps & LLMOps
- Implement and maintain CI/CD pipelines for ML and LLM systems.
- Set up experiment tracking, model monitoring, drift detection, observability, and automated retraining pipelines.
- Enforce model governance, versioning, auditability, and lifecycle management aligned with enterprise standards.
Architecture & Collaboration
- Make sound AI architecture decisions, balancing performance, scalability, security, cost, and time‑to‑value.
- Collaborate closely with data, platform, cloud, security, and product teams to integrate AI into enterprise systems.
- Mentor junior engineers and review AI designs, patterns, and implementation approaches.
Required Skills & Experience
- 3–12 years of relevant experience in AI/ML engineering or applied AI development.
- Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow).
- Hands‑on experience with Generative AI frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel.
- Experience with vector databases, cloud platforms (AWS, Azure, GCP), Docker, and Kubernetes.
- Practical exposure to MLOps/LLMOps tooling (MLflow, Kubeflow, Azure ML, SageMaker, etc.).
- Working knowledge of workflow automation and low‑code/no‑code platforms used for AI‑driven orchestration and rapid solution delivery.
Preferred Qualifications
- Experience building agentic AI systems, AI co‑pilots, or intelligent automation for enterprise workflows.
- Exposure to Responsible AI, governance, security, and observability practices.
- Cloud or AI certifications.
- Open‑source contributions or involvement in AI engineering communities.
Education
- B.Tech / B.E. in Computer Science, AI, Data Science, or related disciplines.
- Master's degree preferred.
Success Measures
- Successful deployment and adoption of AI and agentic workflow solutions.
- Reliability, performance, and cost efficiency of AI systems in production.
- Faster turnaround from business problem to AI solution in a dynamic environment.
- Quality, reuse, and scalability of AI platforms, workflows, and co‑pilot patterns delivered.
Why Join Us
- Build cutting‑edge AI across LLMs, agents, and intelligent workflows.
- Solve real, high‑impact business problems in a fast‑moving enterprise environment.
- Strong learning, growth, and technical leadership opportunities.
- Play a key role in enterprise AI transformation and adoption.
