AI Engineer

About the Role:

• We are seeking a highly skilled Python Generative AI Developer with 4 to 7 years of software development experience to design, develop, and deploy AI-powered applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and modern cloud technologies. The ideal candidate will have strong Python expertise, hands-on experience with Generative AI frameworks, and a solid understanding of scalable backend architectures.
  
• Experience with DevOps practices and cloud deployment is a strong plus.

Key Responsibilities:

• Design, develop, and maintain Generative AI applications and services using Python.
• Build and optimize LLM-powered solutions using frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen, or similar.
• Develop Retrieval-Augmented Generation (RAG) pipelines using vector databases and embedding models.
• Integrate AI solutions with enterprise applications, APIs, databases, and third-party services.
• Fine-tune, evaluate, and optimize AI models for performance, accuracy, and cost efficiency.
• Create and maintain RESTful APIs and microservices for AI workloads. Collaborate with product managers, data scientists, and engineering teams to deliver AI-driven solutions.
• Implement monitoring, logging, observability, and security best practices.
• Stay current with emerging AI technologies, models, and industry trends.
  

Required Skills & Qualifications:

• Technical Skills
  - 4 to 7 years of professional software development experience.
  - Strong proficiency in Python and related frameworks.
  - Hands-on experience with Generative AI and Large Language Models (LLMs).
  - Experience working with OpenAI, Anthropic, Gemini, Llama, Mistral, or similar models.
  - Experience with LangChain, LlamaIndex, CrewAI, AutoGen, Semantic Kernel, or equivalent frameworks.
  - Strong understanding of Prompt Engineering, RAG architectures, Vector Databases, Embeddings and Semantic Search, AI Agents and Tool Calling.
  - Experience developing APIs using FastAPI, Flask, or Django.
  - Knowledge of SQL and NoSQL databases.
  - Experience with Git and modern software development practices.

• Cloud & Deployment
  - Experience working with cloud platforms such as AWS, Azure, or GCP.
  - Understanding of containerization technologies such as Docke

• Good to Have (DevOps & Infrastructure)

- Knowledge of DevOps practices and CI/CD pipelines.
- Experience with Kubernetes.
- Experience with Jenkins, GitHub Actions, GitLab CI/CD, or Azure DevOps.
- Experience with Infrastructure as Code (Terraform, CloudFormation).
- Experience with monitoring tools such as Prometheus, Grafana, ELK Stack, or Datadog.
- Understanding of cloud-native deployments and scalability concepts.
- Experience deploying AI/ML workloads in production environments.

Preferred Qualifications:

• Experience with model evaluation frameworks and LLM observability tools.
• Familiarity with MLOps concepts and workflows.
• Exposure to AI governance, security, and responsible AI practices.
• Experience working in Agile/Scrum environments.
• Excellent problem-solving and communication skills.