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M

Software Engineer - Niche 3 (35) - GenAI Engineer

Mindlance
🇮🇳 India
Hybrid
5 days ago
  • 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
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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

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