Artificial Intelligence Engineer

Job Title: GenAI / Agentic AI Engineer

(Alternate titles: AI Solutions Engineer, Conversational AI Engineer, or Generative AI Specialist)

Location: [Remote / Hybrid]

Experience Level: 5+ years (with 1 - 2 years in GenAI or Agentic AI projects)

Job Summary

GenAI / Agentic AI Engineer

Key Responsibilities

• Design, develop, and deploy GenAI-powered solutions

  using LLMs like OpenAI GPT, Anthropic Claude or Llama .
• Build and configure

  Agentic AI systems

  that can plan, reason, and interact with APIs, databases, and automation platforms.
• Develop and optimize

  prompt chains

  ,

  retrieval-augmented generation (RAG)

  pipelines, and

  context-aware memory architectures

  for dynamic agent behavior.
• Integrate AI agents with

  automation and orchestration tools

  such as UiPath, Power Automate, AWS Lambda, or custom APIs.
• Write clean, maintainable code in

  Python

  and

  C#

  for backend integrations, automation bridges, and service deployments.
• Collaborate with business and technical teams to identify use cases suitable for AI-driven automation.
• Implement

  AI governance, compliance, and data security standards

  (HIPAA, SOC 2, GDPR as applicable).
• Fine-tune or customize models for Healthcare domain-specific applications
• Monitor, evaluate, and optimize model performance, latency, and cost efficiency.
• Create

  proof of concepts (POCs)

  and transition them into

  production-grade solutions

  .

Required Skills & Experience

• Strong hands-on experience with

  Generative AI frameworks and APIs

  (OpenAI, Anthropic, Hugging Face, Azure OpenAI, or Vertex AI).
• Proficiency in

  Python

  (for AI development, integration, and automation) and

  C#

  (for enterprise and backend systems).
• Knowledge of

  Agentic AI frameworks

  (LangChain, AutoGen, CrewAI, Semantic Kernel).
• Experience building

  AI copilots

  , or

  autonomous multi-agent systems

  .
• Strong understanding of

  prompt engineering

  ,

  context management

  , and

  reasoning-based agent orchestration

  .
• Familiarity with

  LLM integration

  in enterprise automation environments (UiPath, Power Automate, AWS Connect, or REST APIs).
• Experience working with

  vector databases

  (Pinecone, FAISS, Weaviate, Chroma) and

  document retrieval (RAG)

  techniques.
• Knowledge of

  cloud AI services

  (AWS AI, Azure Cognitive Services, GCP Vertex AI).
• Excellent debugging, problem-solving, and cross-functional collaboration skills.

Good-to-Have Skills

• Experience in

  Healthcare

  AI solutions (e.g., RCM, claims, or benefit verification).
• Familiarity with

  Docker, REST APIs, microservices, and serverless architectures

  .
• Understanding of

  guardrails, LLMOps, and model evaluation

  frameworks.
• Basic exposure to

  machine learning lifecycle tools

  (MLflow, DVC).

Education

• Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, or related field.
• Certifications in

  AI / GenAI / Cloud AI platforms

  (OpenAI, Azure, AWS) are an advantage.