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Sr. Software Engineer

3 - 7 years

13 - 18 Lacs

bengaluru

Posted:-1 days ago| Platform: Naukri logo

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Skills Required

computer science product engineering gcp artificial intelligence machine learning information retrieval open source monitoring auditing python

Work Mode

Work from Office

Job Type

Full Time

Job Description

  • Collaborate with cross-functional teams (Product, Engineering, CloudOps, BI, and internal stakeholders) to define use cases and deliver value.
  • Design, build, and optimize RAG pipelines (document ingestion, embeddings, hybrid retrieval, re-ranking).
  • Implement and orchestrate agentic AI workflows with tool integration.
  • Build evaluation frameworks: golden sets, hallucination testing, red-teaming, prompt injection defense.
  • Develop guardrails: policy checks, PII redaction, safety filters.
  • Manage LLMOps: caching, model routing, cost/latency optimization.
  • Support LLM fine-tuning (LoRA, PEFT) and dataset preparation.
  • Collaborate with AI Product Engineer to deploy reliable prototypes.
  • Harden PoCs into customer-facing systems with CI/CD, monitoring, rollback strategies.
  • Implement security and compliance controls for SOC2, GDPR, HIPAA, and industry-specific standards.

Desired Skills (High Priority Low Priority)
  • Ability to integrate with BI/data pipelines (Snowflake, Tableau, Informatica, or equivalent).
  • RAG Expertise: Document ingestion, chunking, embeddings generation, retrieval metrics (recall@k, nDCG).
  • Agentic AI Orchestration: LangChain, Autogen, CrewAI, function/tool calling.
  • Evaluation & Guardrails: Golden sets, hallucination testing, red-teaming, prompt injection defense.
  • Vector Databases: Pinecone, Weaviate, pgvector, FAISS.
  • LLMOps & Optimization: Model routing (small large), caching, latency/cost SLOs.
  • Applied NLP: Text classification, summarization, sentiment, entity extraction.
  • LLM Training: Instruction fine-tuning (LoRA/PEFT), dataset curation, data skewness and redundancy evaluations.
  • Python Engineering: Strong in orchestration (pydantic/FastAPI), Pandas for data prep.
  • Security/Compliance Awareness: RBAC, data privacy, audit logging.
  • MLOps/Data Pipelines: Airflow, dbt, or lightweight ETL for ingest.
  • Production Monitoring: Prometheus, Grafana, Datadog dashboards for AI workloads.

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