Sr. Software Engineer (GenAI)

Overview

Mission: Own the intelligence and reliability of our AI stack — retrieval quality, evaluation, guardrails, and model operations. Ensure prototypes are accurate, safe, efficient, and production-ready. These roles are key to accelerating AI adoption across the organization by rapidly prototyping solutions and scaling those that deliver impact. Phase 1: Rapid prototyping for internal validation. Phase 2: Scaling validated prototypes into secure, production-ready, customer-facing applications.

Responsibilities

• 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.

Education

Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, Artificial Intelligence, or related field. Advanced degree (MSc, PhD) in NLP, ML, or Information Retrieval is a plus but not mandatory.

Good-to-Have Qualifications

• Experience fine-tuning or training transformer models (LLMs, BERT, GPT variants).

• Knowledge of search/retrieval systems (Elasticsearch, OpenSearch, Vespa).

• Contributions to open-source AI/ML projects (LangChain, LlamaIndex, HuggingFace).

• Experience with benchmark datasets (SQuAD, GLUE, MS MARCO, or internal corpora).

• Hands-on with cloud AI services (AWS Bedrock, Azure OpenAI, GCP Vertex AI).

• Background in data security, compliance, and privacy standards (SOC2, GDPR, HIPAA).

• Strong math/ML foundation (optimization, statistics, linear algebra).

HYBRID – This role will be based out of The Leela Office located on the 4th Floor, Airport Road, Kodihalli, Bangalore- 560008. Our expectation at this time, is that you would work from our office on Tuesdays, Wednesdays, Thursdays with flexibility to work from home on Mondays and Fridays.