Agentic & Generative AI developer
This role focuses on architecting and deploying production-grade Agentic AI solutions using frameworks like LangChain and LangGraph, with a strong emphasis on scalable RAG pipelines and cloud-based GenAI workflows. The candidate will bridge AI design with engineering execution, ensuring high-performance, reliable deployments while collaborating cross-functionally to standardize reusable AI components.
Skills required
About this role
We are looking for a skilled and delivery-focused Technical Developer to design, build, and deploy innovative AI applications leveraging Agentic AI frameworks and GenAI technologies. The candidate will be responsible for transforming architecture into scalable solutions, optimizing AI workflows, and driving production-grade implementations.
Key Responsibilities:
- Develop, test, and maintain Agentic AI workflows using Langchain, LangGraph, Google ADK, etc.
- Convert AI architecture into working prototypes and deployable components.
- Build and enhance RAG pipelines, agent-based applications, and knowledge retrieval systems.
- Perform data preprocessing, feature engineering, and model optimization.
- Collaborate with teams to build reusable GenAI modules and enforce coding best practices.
- Manage cloud-based deployments on AWS, Azure, or GCP.
- Debug and resolve system bottlenecks, ensuring performance and reliability.
- Keep up with advancements in GenAI, LLMs, and autonomous agents.
Qualifications
Required Skills & Qualifications:
- Strong coding background in Python with AI/ML engineering experience.
- Proven track record of working with LLMs, Transformer models, and Agentic AI frameworks.
- Familiarity with prompt engineering, API integration, and model orchestration.
- Hands-on experience with RAG architectures, vector databases, and agent-to-agent communication (MCP).
- Working knowledge of SQL/NoSQL, data warehousing, and analytical querying.
- Understanding of CI/CD pipelines, Docker, and Kubernetes for model lifecycle management.
Preferred Qualifications (Good to Have):
- Experience with LangGraph agent orchestration or similar agentic toolkits.
- Exposure to enterprise-grade AI applications in finance, healthcare, or e-commerce.
Experience Level:
- 3–7 years of relevant experience in AI/ML or full-stack AI solution development.
Education:
- Bachelor’s or Master’s in Computer Science, Engineering, Data Science, or related field.
Additional Information
All your information will be kept confidential according to EEO guidelines.
