AI Engineer

Experience:

• 3-10 years of experience in AI/ML development, including practical deployment of GenAI and LLM-based solutions.
• Hands-on experience with agentic AI frameworks and autonomous systems.
• Strong understanding of design and architectural patterns for AI applications.
• Exposure to CI/CD and cloud-based orchestration pipelines.
• Familiarity with data security, governance, and compliance standards.
• Experience with Power BI, Azure Data Factory, or Databricks is a plus.
• Prior work in Agile or DevOps environments preferred.

Skills & Competencies:

• Technical Skills

• Programming

  : Proven expertise in Python and/ or C#/.NET

• Agentic / LLM App Development Frameworks

  : Proven expertise in frameworks like Google ADK / CrewAI / LangChain, LangGraph / DSPy

• Machine Learning

  : Supervised/unsupervised learning, NLP, computer vision, deep learning, LLMs

• Generative AI

  : OpenAI APIs, Hugging Face, LangChain, RAG pipelines

• MLOps

  : MLflow, Kubeflow, Azure ML, AWS SageMaker

• API Development

  : Flask, FastAPI, ASP.NET Core Web API

• Version Control

  : Git, GitHub, Azure Repos

• JavaScript / TypeScript Framework:

  Experience with ReactJS / Angular / Vue will be a plus.

• Cloud Platforms

  : Exposure to cloud technologies (like Azure, AWS, GCP) will be a plus - Azure being the preferred provider

• Deployment & CI/CD

  : Familiarity with Docker, Kubernetes, Azure DevOps, GitHub Actions

• Integration

  : Experience with ML.NET, gRPC, REST APIs will be a plus.

Key Responsibilities:

• Architect and implement enterprise-grade AI solutions

  leveraging GenAI, LLMs, and agentic frameworks to solve complex business challenges and automate workflows.

• Develop APIs and microservices

  in Python or C#/.NET to integrate trained models seamlessly into enterprise systems.

• Containerize and orchestrate AI components

  using Docker and Kubernetes for scalable deployments.

• Operationalize ML models

  in collaboration with data scientists, ensuring reproducibility and production readiness.

• Expose models as REST APIs or event-driven endpoints

  , enabling integration with web applications and automation systems.

• Design and manage end-to-end ML workflows

  , including data ingestion, model training, and deployment.

• Implement MLOps best practices

  using MLflow, Kubeflow, Azure ML, AWS SageMaker, or Vertex AI.

• Integrate models into CI/CD pipelines

  in partnership with DevOps teams for continuous delivery.

• Ensure compliance with governance, security, and ethical AI standards

  , including GDPR and HIPAA.

• Communicate technical insights and business impact

  effectively to non-technical stakeholders.

• Evaluate emerging tools and frameworks

  , particularly in multimodal AI, generative models, and agentic systems.
• Contribute to

  internal AI libraries, reusable templates, and automation frameworks

  .
• Work in an

  agile environment

  following Scrum or Kanban methodologies.