Job Description:

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:

1. 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).

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.