Software Engineer - Niche 3 (35) - GenAI Engineer
- LangSmith
- AWS
- GCP
- AI
- Databricks
- Snowflake
- Devops
- CI/CD
- Jenkins
- GitHub Actions
- Azure DevOps
- IaC
- Terraform
- Machine Learning
- Python
- LangChain
- LangGraph
- Semantic Kernel
- AutoGen
- RAG
- Pinecone
- Weaviate
- FAISS
- pgvector
- OpenAI
- Azure
- AWS Bedrock
- MLOps
Required Skills
• Agentic Development/ Observability: Hands on experience with tools such as LangSmith/
Langfuse/Data Dog. Building Multi-agent services. Exposure to monitoring and tracing multi-
agent/LLM workflows concepts, tool-call tracing, latency/cost/drift monitoring, Smart LLM
routing, Cost Optimization and Multi-Cloud Decisioning (AWS/GCP, etc)
• Agentic Governance: guardrails and safety controls for autonomous agents, human-in-the-
loop design, audit logging, and responsible AI/compliance frameworks.
• Data Strategy: experience contributing to or advising on enterprise data strategy, data
architecture roadmaps, and data-driven decision-making frameworks.
• Dev Data Platforms: data pipeline design, and data engineering fundamentals is mandatory.
Hands-on experience with modern data platform tooling (e.g., Databricks, Snowflake, or
similar lakehouse/warehouse platforms) is a plus.
• DevOps: Exposure to CI/CD pipeline design (e.g., Jenkins, GitHub Actions, Azure DevOps),
infrastructure as code (Terraform or equivalent), and automated testing/release practices.
Core GenAI Engineering Skills
• Strong programming skills in Python (or similar) for building GenAI applications.
• Experience with LLM application frameworks such as LangChain, LangGraph, Semantic Kernel,
or AutoGen.
• Experience with RAG architectures, vector databases (e.g., Pinecone, Weaviate, FAISS,
pgvector), and embeddings.
• Familiarity with prompt engineering, fine-tuning/instruction-tuning, and evaluation of LLM
outputs.
• Experience integrating with major LLM providers/platforms such as OpenAI, Anthropic, Azure
OpenAI, or AWS Bedrock.
• Understanding of MLOps/LLMOps practices for model deployment and lifecycle management.
Education
Typically requires a Bachelors Degree and minimum of 5 years directly relevant work experience Note: One of the following alternatives may be accepted: PhD or Law + 3 yrs; Masters + 4 yrs; Associates degree + 6 yrs; High School + 7 yrs.
Bachelors Degree and minimum of 5 years directly relevant work experience
Software Engineer - Niche 3 (35) - GenAI Engineer · Mindlance