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Senior Generative AI Engineer
Minisoft Technologies LLCBengaluru, Karnataka, India2 days ago
Job descriptionFollow 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. 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. 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. 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. 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. 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.
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 :
Metrics & Reporting :
Required Knowledge, Skills, and Abilities :
Proven Delivery :
Programming Expertise :
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 :
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
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Generative Ai Engineer • Bengaluru, Karnataka, India
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