AIML Engineer

Roles & Responsibilities

• 3-6 yrs into Development and deployment AI/ML models for use cases such as prediction, recommendation, classification, and anomaly detection across business functions.
• Build agentic AI systems that can autonomously execute multi-step workflows (e.g., data ingestion, reasoning, decision-making, and action execution) using modern agent frameworks.
• Design, fine-tune, and evaluate LLM-based solutions for tasks like document understanding, question answering, summarization, and conversational assistants.
• Implement Retrieval-Augmented Generation (RAG) pipelines using vector databases to ground LLMs on domain-specific content and internal knowledge bases.
• Collaborate with product managers, domain experts, data engineers, and software developers to translate business requirements into robust AI solutions and APIs.
• Develop and optimize prompt engineering strategies, including structured prompts, tool-calling, and multi-agent coordination for reliable outcomes.
• Integrate AI models and services into existing platforms via REST/GraphQL APIs, microservices, and event-driven architectures.
• Apply MLOps best practices for experiment tracking, versioning, CI/CD, monitoring, and observability of models and AI agents in production.
• Ensure strong data governance, privacy, and security practices when working with sensitive or proprietary data.
• Stay current with advancements in AI/ML, LLMs, agentic AI, and related tools to continuously improve solution quality and performance.

Desired Skills:

AI/ML Core

• Solid understanding of supervised, unsupervised, and deep learning techniques; familiarity with reinforcement learning is a plus.
• Hands-on experience with common ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or XGBoost.
• Experience working with structured and unstructured data, including feature engineering, model evaluation, and performance optimization.

LLMs, NLP & Agentic AI

• Experience fine-tuning and deploying LLMs (e.g., GPT-family models, Llama, Claude, Mistral or similar) for real-world applications.
• Familiarity with embeddings, vector stores, and RAG architectures to enable semantic search and context-aware responses.
• Practical experience with agentic AI frameworks (e.g., LangChain, LangGraph, LlamaIndex, AutoGPT, CrewAI, or similar) and workflow orchestration tools.
• Strong skills in prompt engineering, prompt evaluation, and designing robust interaction patterns for LLM-based systems.

Programming, Data & MLOps

• Strong proficiency in Python and its data ecosystem (NumPy, Pandas, SciPy), and experience with FastAPI or Django for backend/API development.
• Experience with SQL and NoSQL databases (e.g., PostgreSQL, MySQL, MongoDB) and working with ETL/data pipelines.
• Familiarity with big data and distributed processing (e.g., Spark) is a plus.
• Experience with containerization and orchestration (Docker, Kubernetes) for deploying AI workloads at scale.
• Exposure to cloud platforms (AWS, Azure, GCP) and their AI/ML services.
• Knowledge of MLOps tools and practices for building reliable, maintainable AI systems in production environments.

Good to Have

• Experience working in any specific domain (e.g., SaaS, analytics, finance, taxation, customer experience, compliance) where AI was embedded into core workflows.

Contributions to internal frameworks, open-source projects, or research related to LLMs, agentic AI, or scalable ML systems