Senior Agentic AI Developer at Kyndryl to build autonomous AI systems for complex tasks. Collaborating to integrate LLMs and achieve real - world business outcomes.
About the role
- AI Engineer developing and deploying generative AI and LLM solutions at DATAECONOMY. Collaborating with teams to implement innovative AI technologies.
Responsibilities
- Architect, develop, and implement LLM-powered and generative AI solutions utilizing both proprietary and open-source technologies (e.g., GPT-4, Llama 3, Gemini, Claude)
- Craft, refine, and test model prompts to achieve targeted outputs
- Build and maintain stateful, multi-agent workflows and autonomous AI agents using frameworks like Microsoft AutoGen, LangGraph, LangChain, LlamaIndex, and CrewAI
- Design and deploy RAG pipelines by integrating vector databases for efficient knowledge retrieval
- Serve LLMs via FastAPI-based endpoints and manage their deployment using Docker containers and orchestration tools like Kubernetes and cloud functions
- Construct data pipelines for ingestion, preprocessing, and controlled versioning of training datasets
- Partner with data scientists, software engineers, and product teams to scope and integrate generative AI initiatives
Requirements
- At least 3 years in machine learning engineering, with 1-2 years focused on building and deploying generative AI or LLM-based applications
- Proficiency in Python and FastAPI, and experience developing RESTful APIs and microservices
- Hands-on familiarity with LLM providers (OpenAI, Anthropic, Google, Meta) and with frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or Transformers
- Proven ability to fine-tune language models and craft effective prompts tailored to specific applications
- Experience creating RAG pipelines with vector databases (e.g., Pinecone, Faiss, Weaviate) and evaluation frameworks like RAGAS
- Practical knowledge of containerization (Docker), orchestration (Kubernetes), and cloud deployments (AWS, Azure, GCP)
- Excellent teamwork and communication skills, able to bridge technical and business perspectives effectively
- Bachelor's degree in Computer Science, Data Science, or a related discipline (Master’s degree preferred)
Benefits
- Hybrid working model | Flexible schedules
- Opportunities for professional development
- Exposure to cutting-edge technologies
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