Job Title: Senior AI Engineer — GenAI Solutions
Location: Hyderabad, India | Employment Type: Full-Time | Experience: 4–7 Years | Reports to: AI Practice Lead
About Viamagus
Viamagus Technologies Pvt. Ltd. is a technology and talent solutions company helping organizations build high-impact engineering and AI capabilities. We are hiring on behalf of a fast-growing AI-first technology services company with delivery teams across the US and India, serving enterprise and Fortune 500 clients across financial services, healthcare, insurance, retail, and SaaS. The client is building a brand-new AI Consulting & Engineering practice, helping enterprises turn the platforms they already run into intelligent, AI-enabled systems tied to measurable business outcomes.
Position Summary
We're looking for a Senior AI Engineer who does two things exceptionally well: builds production-grade GenAI applications, and sits in front of clients to shape what gets built. Expect roughly 70% hands-on engineering and 30% client-facing solutioning. You'll take AI use cases from discovery workshop to deployed, monitored production system — LLM copilots, RAG pipelines, and agentic workflows that live inside enterprise clients products and platforms. This is real production work for enterprise and Fortune 500 clients, not disconnected proofs of concept. You'll be one of the founding engineers of this practice.
Key Responsibilities
- Design and build production GenAI applications — LLM copilots, RAG pipelines, agentic workflows, and AI-native UX — on a modern 2026 AI stack
- Engineer retrieval end to end: chunking, embeddings, vector stores, retrieval, re-ranking, and hybrid search; debug retrieval quality when it matters
- Build agents and multi-step workflows with LangGraph and LangChain; integrate MCP servers and agent SDKs for tool access
- Augment enterprise platforms with predictive insight, intelligent automation, and conversational UX
- Deploy to AWS Bedrock, Azure AI Foundry, or GCP Vertex AI with solid CI/CD, observability, and guardrails
- Build evaluation harnesses, wire up LLM observability, and bake in responsible-AI basics: PII handling, guardrails, and audit trails
- Run client discovery workshops and translate business problems into scoped, ROI-backed AI use cases
- Demo working software to technical and business stakeholders, and defend design decisions and tradeoffs
- Contribute to reusable accelerators and mentor junior engineers as the practice grows
Required Skills Mandatory
- 4–7 years of software engineering experience with real production systems
- Python mastery — clean, testable, production Python (FastAPI or similar)
- Production GenAI experience — shipped at least one real LLM-powered feature: prompt design, structured outputs, tool/function calling, streaming
- RAG engineering — chunking, embeddings, vector databases (Pinecone, Weaviate, pgvector, or Qdrant), retrieval, re-ranking, hybrid search
- Agentic systems — built agents or workflows with LangGraph, LangChain, or LlamaIndex; understands MCP and agent SDKs
- Full-stack capability — TypeScript/React/Next.js, able to ship a working product, not just an API
- Cloud AI deployment — hands-on with AWS Bedrock, Azure AI Foundry, or GCP Vertex AI, plus Docker and CI/CD
- Evaluation & trust — eval harnesses, LLM observability (LangSmith, Langfuse, or Arize), responsible handling of PII and guardrails
- Client-facing communication — can run a workshop, scope a use case, demo to stakeholders, and articulate ROI; fluent written and spoken English
Preferred
- Experience adding AI to enterprise platforms such as Clarity PPM, Medallia, ServiceNow, or Salesforce Einstein
- Deeper MLOps — MLflow, Weights & Biases, model monitoring, serverless GPU
Nice to Have
- Data & RAG infrastructure — ETL/ELT, CDC, orchestration, streaming, knowledge graphs, Databricks or Snowflake
- Fine-tuning & multimodal experience — LoRA, distillation, vision/voice/multimodal applications
- Industry domain depth in financial services, insurance, healthcare, or retail
- AI governance awareness (NIST AI RMF, EU AI Act) and relevant cloud AI/ML certifications
Experience Required
- 4–7 years of software engineering experience, including demonstrated production GenAI, RAG, and agentic systems work
Why Join
- Founding role — help shape the architecture, standards, and culture of a new AI practice with real autonomy
- Production, not pilots — ship AI that goes live for enterprise and Fortune 500 clients
- Outcomes over hype — every engagement is tied to a business KPI
- Modern stack, real depth — work across leading 2026 AI tooling with a team that ships to production
- Mentorship and growth — learn directly from senior full-stack and AI leadership
