AI / ML Engineer

The Core Responsibilities For The Job Include The Following

ML and Predictive Systems Development:

• Design, develop, and deploy enterprise-grade machine learning models for recommendations, predictions, and personalization use cases.
• Work on problems such as churn prediction, intelligent routing, anomaly detection, and behavior modeling.
• Leverage techniques in supervised, unsupervised, and reinforcement learning as needed based on business context.
  

LLMs And Generative AI

• Build and fine-tune LLM-based solutions (e. g., GPT, LLaMA, Claude, or open-source models) for tasks such as summarization, semantic search, document understanding, and copilots.
• Deliver production-ready GenAI projects, applying techniques like RAG (Retrieval-Augmented Generation), prompt engineering, fine-tuning, and vector search (e. g., FAISS, Pinecone, Weaviate).
• Collaborate with engineering to embed LLM workflows into enterprise applications, ensuring scalability and performance.
  

Agentic AI And Ecosystem Engagement

• Contribute thought leadership and experimentation around Agentic AI architectures, task orchestration, memory management, tool integration, and decision autonomy.
• Stay ahead of trends in the open-source and commercial LLM/AI space, including LangChain, AutoGen, DSPy, and ADK-based systems.
• Develop internal PoCs or evaluate frameworks to assess viability for enterprise use.
  

Collaboration And Delivery

• Work with cross-functional teams to identify AI opportunities and define technical roadmaps.
• Translate business needs into data science problems, define success metrics, and communicate results to stakeholders.
• Ensure model governance, monitoring, and explainability for AI systems in production.
  

Requirements

• Master's or PhD in Computer Science, Data Science, Statistics, or related field.
• 5-8 years of experience in data science and ML, with strong enterprise project delivery experience.
• Proven success in building and deploying ML models and recommendation systems at scale.
• 2+ projects delivered involving LLMs and Generative AI, with hands-on experience in one or more of: OpenAI, Hugging Face Transformers, LangChain, Vector DBs, or model fine-tuning.
• Advanced Python programming skills and experience with ML libraries (e. g., Scikit-learn, XGBoost, PyTorch, TensorFlow).
• Experience with cloud-based ML/AI platforms (e. g., Vertex AI, AWS SageMaker, Azure ML).
• Strong understanding of system architecture, APIs, data pipelines, and model integration patterns.
  

Preferred Qualifications

• Experience with Agentic AI frameworks and orchestration systems (LangChain, AutoGen, ADK, CrewAI).
• Familiarity with prompt optimization, tool chaining, task planning, and autonomous agents.
• Working knowledge of MLOps best practices, including model versioning, CI/CD for ML, and model monitoring.
• Strong communication skills and ability to advocate for AI-driven solutions across technical and non-technical teams.
• Regular follower of AI research, open-source trends, and GenAI product developments.