AI/ML Engineer

Join Our team as an AI/ML Engineer working on cutting-edge enterprise AI solutions. You'll build AI-powered prototypes and production systems that deliver measurable business value across software development lifecycle acceleration, intelligent automation, and operational excellence. This role offers the opportunity to work with the latest GenAI technologies while solving real-world enterprise challenges.

What You’ll Do

• Design and implement AI/ML models for diverse use cases including code generation, test automation, documentation, and predictive analytics.
• Build GenAI‑powered applications using LLMs (GPT‑4, Claude, Llama, etc.).
• Develop multi‑agent orchestration systems using supervisor‑worker patterns.
• Create stateful, iterative reasoning pipelines for complex AI workflows.
• Implement Model Context Protocol (MCP) integrations for tool and data access.
• Build and optimize MLOps pipelines for model training, deployment, and monitoring.
• Integrate AI solutions with enterprise systems and existing data sources.
• Implement observability, governance, and evaluation frameworks for AI systems.
• Work with cloud AI/ML services (AWS SageMaker, Azure ML, GCP Vertex AI).
• Develop proof‑of‑concept AI solutions to validate use cases in 2‑week sprints.
• Conduct A/B testing and performance benchmarking of different AI approaches.
• Optimize models for cost, latency, and accuracy based on business requirements.
• Create demos and prototypes for stakeholder presentations.
  
  

What You Know

• 4+ years of software engineering experience with 2+ years in AI/ML.
• Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, JAX).
• Hands‑on experience with LLM APIs (OpenAI, Anthropic, open‑source models).
• Experience building production ML systems and MLOps pipelines.
• Solid understanding of NLP, deep learning, and transformer architectures.
• Proficiency with vector databases (Pinecone, Weaviate, ChromaDB) and RAG systems.
• Experience with containerization (Docker) and orchestration (Kubernetes).
• Practical experience with prompt engineering and LLM optimization.
• Knowledge of fine‑tuning techniques (LoRA, QLoRA, PEFT).
• Understanding of AI agent frameworks (LangChain, LangGraph, CrewAI, AutoGen).
• Familiarity with model evaluation metrics and benchmarking methodologies.
• Experience with MLOps tools (MLflow, Weights & Biases, or similar).
  
  

Education

• Bachelor’s degree in computer science, Engineering, Data Science, or a related field