What are the responsibilities and job description for the Agentic AI & Generative AI Engineer position at Career Soft Solutions Inc?
Job Title: Agentic AI & Generative AI Engineer
Location: Onsite - Richardson, TX and Charlotte, NC
Contract on W2
Key Responsibilities
- Design, build, and deploy Agentic AI solutions capable of autonomous decision-making and multi-step reasoning.
- Develop Generative AI applications using Large Language Models (LLMs) such as GPT-4/5, Claude, Gemini, and Llama.
- Build multi-agent systems using frameworks like LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar.
- Implement Retrieval-Augmented Generation (RAG) pipelines using vector databases and enterprise knowledge repositories.
- Develop AI copilots, intelligent assistants, chatbots, and workflow automation solutions.
- Integrate AI applications with REST APIs, enterprise applications, databases, and cloud services.
- Fine-tune prompt engineering strategies to improve response quality, reasoning, and accuracy.
- Design agent memory, planning, orchestration, and tool-calling capabilities.
- Deploy AI workloads on Azure, AWS, or Google Cloud using containerized architectures.
- Optimize inference performance, latency, scalability, and cost.
- Implement AI governance, security, responsible AI, and compliance best practices.
- Monitor model performance and continuously improve AI systems using user feedback and evaluation metrics.
- Collaborate with product owners, architects, data scientists, and software engineers throughout the AI development lifecycle.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or related field.
- 5 years of software engineering experience.
- 2 years of hands-on experience building Generative AI or LLM-powered applications.
- Strong programming skills in Python.
- Experience with OpenAI, Anthropic Claude, Gemini, Llama, or other foundation models.
- Strong understanding of Prompt Engineering and LLM optimization.
- Experience building RAG applications.
- Experience with Vector Databases such as Pinecone, Weaviate, Chroma, FAISS, Milvus, or Azure AI Search.
- Experience with Lang Chain, LangGraph, CrewAI, AutoGen, or Semantic Kernel.
- Knowledge of embeddings, chunking, semantic search, and retrieval optimization.
- Experience integrating AI solutions with REST APIs and enterprise applications.
- Strong understanding of Docker, Kubernetes, CI/CD pipelines, and Git.
- Experience deploying AI solutions on Azure, AWS, or Google Cloud.
Preferred Qualifications
- Experience fine-tuning open-source LLMs.
- Knowledge of Model Context Protocol (MCP).
- Experience with AI agent orchestration platforms.
- Familiarity with AI observability tools such as LangSmith, Phoenix, Weights & Biases, or MLflow.
- Experience with Azure AI Foundry, Azure OpenAI, Amazon Bedrock, or Google Vertex AI.
- Knowledge of knowledge graphs and graph databases (Neo4j).
- Experience implementing Responsible AI and AI governance frameworks.
- Experience working with structured and unstructured enterprise data.