Exponentia.ai - AI Engineer

Role Overview: We are seeking a highly skilled AI Engineer with hands-on experience in AI/ML, Generative AI, and Agentic AI systems. The ideal candidate will have a deep understanding of AI agent frameworks, Model Context Protocol (MCP), Databricks, and cloud-native MLOps workflows. This role involves building, deploying, and optimizing intelligent systems from LLM-powered applications to autonomous multi-agent architectures.

Core Responsibilities

• Design, develop, and deploy machine learning and generative AI models in production environments.
• Build and integrate agentic AI systems - intelligent agents capable of reasoning, planning, and multi-step decision-making.
• Develop and maintain data pipelines and MLOps workflows using Databricks, MLflow, and cloud-native tools.
• Integrate LLMs and AI agents with external APIs, databases, and tools using agent frameworks (LangChain, AutoGen, CrewAI, Semantic Kernel, LangGraph).
• Implement and manage Model Context Protocol (MCP) connections between agents and enterprise systems.
• Optimize AI workloads in AWS, Azure, or GCP environments with scalable, secure infrastructure.
• Collaborate with cross-functional teams (data, cloud, and product) to deliver AI-driven solutions.
• Ensure AI system security, observability, explainability, and compliance with governance standards.

Key Technical Skills

• AI / ML & Data Science:
• Strong foundation in machine learning, deep learning, and data science concepts.
• Expertise in Python with ML libraries: PyTorch, TensorFlow, scikit-learn, pandas, NumPy.
• Knowledge of model evaluation, feature engineering, and transfer learning.
• Experience with vector databases (FAISS, Pinecone, ChromaDB).
• Generative AI & NLP:
• Hands-on experience with LLMs, prompt engineering, RAG (Retrieval-Augmented Generation), and fine-tuning.
• Familiarity with LangChain, LlamaIndex, or similar orchestration frameworks.
• Implementation of text generation, summarization, classification, and document Q&A systems.
• Agentic AI, Agent Frameworks & MCP:
• Deep understanding of agentic AI systems - autonomous, multi-step, tool-using architectures.
• Practical experience building AI agents using frameworks such as LangChain, AutoGen, CrewAI, LangGraph, or Semantic Kernel.
• Experience designing multi-agent collaboration systems and task orchestration.
• Hands-on experience implementing or integrating Model Context Protocol (MCP) for tool invocation, context sharing, and agent-to-system communication.
• Strong awareness of safety, governance, auditability, and agent evaluation frameworks.
• Databricks, MLOps & Data Engineering:
• Strong experience with Databricks (Spark, Delta Lake, MLflow, feature store).
• End-to-end experience in data pipelines, ETL/ELT processes, and real-time streaming.
• Proficiency in MLOps best practices: model registry, versioning, drift detection, rollback.
• Observability and automation for deployed ML systems.
• Cloud & Infrastructure:
• Hands-on with AWS / Azure / GCP for AI workloads.
• Familiarity with SageMaker, Azure ML, or Vertex AI.
• Experience with Docker, Kubernetes, and serverless deployments.
• Working knowledge of Infrastructure as Code (Terraform / CloudFormation) and CI/CD pipelines.
• Software Engineering & APIs:
• Strong programming and software design fundamentals.
• Experience building REST / GraphQL APIs and microservices.
• Event-driven and asynchronous architectures (Kafka, Pub/Sub, message queues).
• Integration of AI components with enterprise software systems.
• Security, Observability & Responsible AI:
• Knowledge of monitoring, logging, and tracing (Prometheus, Grafana, OpenTelemetry).
• Implementation of secure AI practices - access control, secrets management, prompt injection defense.
• Understanding of model explainability, bias mitigation, and ethical AI considerations.

Preferred / Nice-to-Have Skills

• Familiarity with reinforcement learning and planning-based agents.
• Experience with knowledge graphs and symbolic reasoning.
• Building multi-modal agents (text + vision + audio).
• Contributions to open-source AI / agent frameworks.
• Exposure to edge or on-device AI systems.

Soft Skills

• Strong analytical and problem-solving skills.
• Excellent communication and documentation abilities.
• Ability to work cross-functionally in fast-paced, research-driven environments.
• Curious mindset with a passion for emerging AI technologies.

(ref:hirist.tech)