Senior Generative AI Engineer

Hi, I hope you are doing well, Role: Senior Generative AI Engineer (LLM & Agent Systems) Location: Bangalore, Remote - (Occasionally 2 times in a month) Experience: 3+ Duration: Long term Need local to Bangalore profiles Job description: AI/ML System Implementation & Integration: Translate requirements into well-engineered components (pipelines, vector stores, prompt/agent logic, evaluation hooks) and implement them in partnership with the platform/architecture team. Application & Agent Development: Build and maintain LLM-based agents/services that securely call enterprise tools (ServiceNow, Salesforce, Oracle, etc.) using approved APIs and tool-calling frameworks. Create lightweight internal SDKs/utilities where needed. RAG & Search Enablement: Configure and optimize RAG workflows (chunking, embeddings, metadata filters) and integrate with existing search/vector infrastructure—escalating architecture changes to designated architects. MLOps & SDLC Practices: Follow and improve team standards for CI/CD, testing, prompt/model versioning, and observability. Own feature delivery through dev/test/prod, coordinating with release managers. Governance, Security & Compliance: Apply established guardrails (PII redaction, policy checks, access controls). Partner with InfoSec and architects to close gaps; document decisions and risks. Metrics & Reporting: Instrument services with KPIs (latency, cost, accuracy/quality) and build lightweight dashboards. Deep BI/reporting not primary. Documentation & Communication: Write clear technical docs (APIs, workflows, runbooks), user stories, and acceptance criteria. Support and sometimes lead UAT/test activities. Collaboration & Mentorship: Facilitate working sessions with stakeholders; mentor junior engineers through code reviews and pair programming; provide concise updates and risk flags. Education & Experience: Bachelor's degree and eight years of relevant experience or a combination of education and relevant experience. Required Knowledge, Skills, and Abilities: Agent/Agentic Framework Experience: Built and shipped at least one production LLM agent or agentic workflow using frameworks such as LangGraph, LangChain, CrewAI/AutoGen, Google Agent Builder/Vertex AI Agents (or equivalent). Able to explain tool selection, orchestration logic, and post‑deployment support. Proven Delivery: Implemented 3+ AI/ML projects and 2+ GenAI/LLM projects in production, with operational support (monitoring, tuning, incident response). Projects should serve sizable user populations and demonstrate measurable efficiency gains. Strong understanding of AI/ML concepts (LLMs/transformers and classical ML) and experience designing, developing, testing, and deploying AI-driven applications. Programming Expertise: Python (primary) plus experience with Node.js/Next.js/React/TypeScript and Java; demonstrated ability to quickly learn new tools/frameworks. Experience with cloud AI stacks (e.g., Google Vertex AI, AWS Bedrock, Azure OpenAI) and vector/search technologies (Pinecone, Elastic/OpenSearch, FAISS, Milvus, etc.). Knowledge of data design/architecture, relational and NoSQL databases, and data modeling. Thorough understanding of SDLC, MLOps, and quality control practices. Ability to define/solve logical problems for highly technical applications; strong problem-solving and systematic troubleshooting skills. Excellent communication, listening, negotiation, and conflict resolution skills; ability to bridge functional and technical resources. Desired Knowledge, Skills, and Abilities: MLOps Tooling: MLflow, Kubeflow, Vertex Pipelines, SageMaker Pipelines; LangSmith/PromptLayer/Weights & Biases. Open Source Savvy: Experience working with, customizing, and improving open-source solutions; comfortable contributing fixes/features upstream. Rapid Tech Adoption: Demonstrated ability to pick up a new technology/framework quickly and deliver production value with it. GenAI Frameworks: LangChain, LlamaIndex, DSPy, Haystack, LangGraph, Agent Engine, Google ADK, AWS AgentCore, CrewAI/AutoGen. Security & Governance: Implementing AI guardrails, red-teaming, and policy enforcement frameworks. Enterprise Integrations: ServiceNow, Salesforce, Oracle Financials, or others. UI Development: React/Next.js/Tailwind for internal tools. Prompt engineering at scale: Structured prompts (JSON/function-calling), templates, version control; automated/offline & online evals (rubrics, hallucination/bias checks, A/B tests, golden sets). Parameter‑efficient fine‑tuning (LoRA/QLoRA/adapters), supervised instruction tuning; hosting open‑weight models (Llama/Mistral/Qwen) with vLLM/TGI/Ollama. Safety/guardrails frameworks (Guardrails.ai, NeMo Guardrails, Azure/AWS safety filters) and jailbreak/drift detection. Hybrid search & reranking (BM25+dense, Cohere/Voyage/Jina rerankers), synthetic data generation, provenance/watermarking. Telemetry & governance: prompt/model drift monitoring, policy‑as‑code, audit logging, red‑teaming playbooks. Certifications and Licenses: Google/AWS/Azure ML/AI certifications or strong demonstrable portfolio of production AI systems. Sameer Dudekula|Lead US IT Technical recruiter Minisoft Technologies LLC +91-7780246304 | sameerd@minisofttech.com