You will be taken to Wissen Technology’s own careers page to apply. Job ID 60622944.
Job description
About Us
Position Name
Python Full Stack Engineer – Argus Integration (GenAI/Agentic AI)
Experience
5–8 years of professional software engineering experience, with strong hands-on expertise in Python and practical experience building LLM/GenAI applications.
Location
Bangalore
Shift Timings
General
Job Description
Design and build agent tools and orchestration on AWS Strands Agents and Bedrock — both deterministic rule tools and LLM-judgment tools grounded in retrieval.
Implement RAG pipelines end to end: ingestion, chunking, embedding, retrieval, and grounding — including knowledge-graph-anchored retrieval where it improves precision.
Write evaluation and test harnesses — deterministic checks, LLM-as-judge evaluators, and regression suites — to measure faithfulness, correctness, and safety.
Add guardrails and human-in-the-loop review hooks appropriate for a compliance-sensitive product.
Build clean integrations with platform and third-party systems (e.g. document sources, Veeva Vault, downstream services).
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Instrument agent behaviour into the observability stack (Langfuse, OpenTelemetry/ADOT) and use traces to debug and optimize cost and latency.
Participate actively in code review, design discussions, and documentation; help level up peers and improve team practices.
Ship production-quality code with strong testing, error handling, and reproducibility as defaults, not afterthoughts.
Required qualifications
5–8 years of professional software engineering with strong, current Python.
Hands-on experience building LLM/GenAI features — RAG, prompt engineering, and agent/tool-use (function calling).
Familiarity with at least one agent framework (Strands Agents, LangGraph, LlamaIndex, AutoGen, CrewAI, or equivalent) and one LLM platform (Bedrock, OpenAI, Anthropic, Vertex).
Solid software fundamentals: data structures, API design, testing, version control, and CI/CD.
Comfort deploying and running services on AWS (or a comparable cloud).
A quality mindset — you test, measure, and reason about LLM correctness rather than eyeballing outputs.
Preferred / nice-to-have
Vector databases (pgvector, Pinecone, Weaviate, Qdrant, Chroma) and retrieval tuning.
Neo4j / knowledge graphs and GraphRAG.
Evaluation frameworks (RAGAS, DeepEval, promptfoo, or the Strands Evals SDK) and LLM-as-judge patterns.
Observability for LLM apps (Langfuse, LangSmith, OpenTelemetry/ADOT).
Exposure to a regulated or human-in-the-loop domain (pharma, healthcare, finance).
Competencies
Cloud Computing COMMUNICATION SKILLS JOB KNOWLEDGE
Adaptability and willingness to learn emerging technologies.
Ownership, accountability, and a proactive approach to resolving issues.
Ability to communicate technical concepts clearly to business and technical stakeholders.
Qualification
Bachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, or a related field, with 5–8 years of relevant professional software engineering experience.
Certifications
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