AI Architect

BMC Software; Bengaluru; On-site; INR 3,500,000 - 6,000,000; Full time

Job summary

BMC Software is seeking an AI Engineer to lead the build of our next-generation Agentic AI platform. You will design, code, and ship production-grade AI capabilities, working with senior leadership to deliver reliable and cost-efficient AI services for external customers.

The role emphasizes ownership, scalability, safety, and operational excellence, with responsibilities spanning from architecture and governance to hands-on implementation in enterprise environments.

Qualifications

• 10+ years of software development experience, shipping B2B products.
• Expert Python and production systems experience.
• Hands-on with LLMs and GenAI in real applications.
• Proven ability to deploy AI-powered products in production.

Responsibilities

• Design and evolve agentic AI systems for production environments.
• Take AI features from concept to production in a true 01 product environment.
• Write and review production-grade Python code across AI pipelines and services.
• Implement prompt engineering, tool use, memory, evaluation, and guardrails as core concerns.
• Design agent frameworks balancing autonomy with observability and safety.
• Deploy AI services across cloud platforms with enterprise integrations.

Skills

Python

Production systems

LLMs & GenAI

Agent frameworks

Prompt engineering

RAG architectures

Vector databases

Model evaluation

Safety controls

Distributed systems

Tools

LangChain

LlamaIndex

Pinecone

Weaviate

FAISS

Milvus

SageMaker

Vertex AI

Kubernetes

Job description

Job Summary

We are looking for an AI Engineer to build our next-generation Agentic AI platform from 01. This is a hands-on, delivery-driven role for a senior engineer who has shipped AI-powered products into real enterprise environments, understands the trade-offs of production AI systems, and takes full ownership of outcomes not just models or demos.

You will work alongside our VP of Engineering, VP of AI and product leadership to define and build the core AI systems of the platform. You will spend most of your time designing, coding, and shipping production-grade AI capabilities used by external B2B customers. Success is measured by reliability, controllability, cost-efficiency, and customer impact not novelty.

Your Contributions to BMC and Your Success

• Design, build, and evolve agentic AI systems that reason, plan, execute, and adapt in production environments.
• Take AI-driven features from concept to production in a true 01 product environment.
• Write and review high-quality production code (Python-first) across AI pipelines, inference services, orchestration layers, and supporting systems.
• Implement prompt engineering, tool use, memory, evaluation, and guardrails as first-class engineering concerns, not experiments.
• Design agent frameworks that balance autonomy with determinism, observability, and safety.
• Make pragmatic architectural trade-offs across latency, cost, accuracy, scalability, and maintainability.
• Integrate and operate LLMs (commercial and/or open-source) including model selection, fine-tuning strategies, embeddings, retrieval (RAG), and inference optimization.
• Address real-world issues: hallucinations, drift, prompt regressions, failure modes, and customer trust.
• Deploy and operate AI services across cloud platforms (AWS, Azure, GCP), including secure enterprise integrations and customer-specific deployments.
• Design scalable inference and orchestration architectures using containers, APIs, and distributed systems.
• Ensure the platform is shippable, debuggable, and supportable not fragile or research-grade.
• Act with founder-level ownership: identify gaps, propose solutions, and move forward without waiting for perfect requirements.

Required Skillset and Experience

• 10+ years of professional software development experience, with significant time shipping B2B products used by external customers.
• Strong software engineering foundation with expert-level Python and experience designing production systems.
• Proven experience building, deploying, and operating AI-powered products in production not just prototypes or research.
• Hands-on experience with LLMs and GenAI systems in real applications (e.g., agents, copilots, automation, decision systems).

Deep understanding of at least several of the following

• Agent frameworks and orchestration
• Prompt engineering and tool-use patterns
• RAG architectures and vector search
• Model evaluation, feedback loops, and monitoring
• Safety, guardrails, and enterprise controls

Hands-on experience with multiple of the following in real systems

• LangGraph and/or LangChain
• LlamaIndex
• Vector databases (e.g., Pinecone, Weaviate, FAISS, Milvus)
• Prompt engineering as a managed, versioned, testable artifact
• Experience deploying and operating LLMs using AWS SageMaker, Vertex AI, or equivalent managed platforms. Direct API integrations (OpenAI, Anthropic)
• Experience designing multi-agent systems or complex agent workflows.
• Experience commercializing AI features under enterprise constraints (security, compliance, uptime).
• Comfort operating in ambiguity and making decisions with incomplete information.

Nice-to-have: develop in the following skills

• Contributions to open-source GenAI tooling or internal frameworks used at scale
• Experience with Supervised fine-tuning, Parameter-efficient tuning methods (LoRA, QLoRA), reinforcement learning (RLHF) and preference optimization (PPO, DPO, GRPO).
• Experience deploying LLMs at scale (Kubernetes, model serving, GPU optimization).