AI EngineerApply Now

📍 Location: Noida | Type: Full-Time, Permanent | Experience: 1–3 years

We're looking for a Junior AI Application Engineer to join our GenAI delivery team and help build production LLM/SLM applications — including for air-gapped and on-prem environments. You'll work on well-scoped pieces of larger features under the guidance of a senior Application Engineer or Technical Program Lead, with plenty of room to grow into full feature ownership.

Key Responsibilities:

• Build defined components of RAG pipelines, agents, and LLM/SLM-backed features from specs handed off by senior engineers.
• Write and test Python code — API endpoints, data pipelines, and integration glue between LLM components and internal systems.
• Assist with fine-tuning, quantizing, and evaluating SLMs under supervision; run benchmarks and document results.
• Help set up and maintain vector store integrations (FAISS, Milvus, Weaviate, Qdrant) and local inference serving (vLLM, Ollama).
• Containerize small services (Docker) and support deployment to cloud/on-prem targets alongside senior team members.
• Keep JIRA stories updated, raise blockers early, and demo completed work in sprint reviews.
• Use Claude Code / AI coding agents as your default way of writing code — learn to write clear specs/prompts and to carefully review and test what the agent produces before it ships.

Required Skills & Experience:

• 1–3 years of professional software engineering experience (internships count toward this).
• Solid Python fundamentals; comfortable reading and debugging someone else's code.
• Some exposure to LLMs/GenAI — coursework, personal projects, hackathons, or prior work experience with LangChain/LlamaIndex, OpenAI/Anthropic APIs, or similar.
• Basic understanding of REST APIs, git, and working in a codebase with others.
• Curious and comfortable using AI coding tools (Claude Code or similar) as part of daily work, with a habit of reviewing generated code rather than accepting it blindly.
• Willingness to learn Docker, cloud basics (AWS/Azure/GCP), and vector databases on the job.

Behavioural Expectations:

• Eager to learn, asks good questions, and takes feedback well — this role is designed to grow you into a full Application Engineer.
• Reliable on sprint commitments; keeps JIRA current and communicates blockers early rather than sitting on them.
• Comfortable working alongside AI agents: writes clear instructions, checks the output carefully, doesn't just copy-paste blindly.
• Team player — collaborates well in a hybrid setup with distributed (US/India/APAC) colleagues.

Good to Have:

• Personal projects, open-source contributions, or hackathon work involving LLMs/GenAI.
• Exposure to Big Data tools (Spark/Hive) or basic ML/data science coursework.
• Any experience, even academic, with model evaluation or prompt engineering.
• Contribution to open source projects, academic papers published, filled patents.

What You'll Gain:

• Direct mentorship from senior Application Engineers and Technical Program Leads on real enterprise GenAI/SLM engagements.
• Hands-on exposure to air-gapped/sovereign AI deployments — a niche, high-value skill set.
• A fast track to owning full features independently within 12–18 months.

Senior AI Engineer Apply Now

📍 Location: Noida | Type: Full-Time, Permanent | Experience: 5+ years

We're looking for a hands-on AI Application Engineer to build and ship the GenAI/SLM applications designed by our Technical Program Leads — including for air-gapped and on-prem environments. RAG pipelines, agents, fine-tuned models, and the APIs/UI that expose them.

Key Responsibilities:

• Build and productionize RAG pipelines, agentic workflows, and LLM/SLM-backed features from architecture specs handed off by the Technical Program Lead.
• Fine-tune, quantize, and package SLMs for constrained/offline environments; benchmark accuracy, latency, and cost against alternatives.
• Implement local/offline inference serving (vLLM, Ollama) and vector store integrations (FAISS, Milvus, Weaviate, Qdrant) for air-gapped deployments.
• Write clean, testable, well-documented Python — APIs, data pipelines, and integration layers connecting LLM components to enterprise systems.
• Containerize and deploy applications (Docker/Kubernetes) across cloud (AWS/Azure/GCP) and on-prem targets.
• Build evaluation harnesses, guardrails, and monitoring/logging for model outputs in line with the governance framework set by the Technical Program Lead.
• Work sprint-to-sprint in JIRA — pick up stories, raise blockers early, keep the board current, and demo working software each sprint.

Required Skills & Experience:

• 5+ years professional software engineering; 2+ years building GenAI/ML applications in production.
• Strong Python; hands-on with LangChain, LlamaIndex, or similar frameworks.
• Practical experience with LLMs/SLMs — prompting, RAG, fine-tuning (LoRA/QLoRA), or model quantization.
• Working knowledge of vector databases and embedding pipelines.
• Comfortable with Docker/Kubernetes and at least one major cloud (AWS/Azure/GCP).
• Expert with Claude-driven development — uses Claude Code / Claude-based agents daily as part of the build workflow; comfortable authoring or using custom Skills/MCP tools to speed up delivery.
• Reviewer, not just implementer: most code is agent-generated first; your core skill is writing tight specs, critically reviewing agent output line-by-line, catching bugs/edge cases/security issues, and deciding when to trust vs. override the agent — rather than manually writing everything from scratch.
• Solid understanding of REST/API design, git workflows, and CI/CD basics.

Behavioural Expectations:

• Execution-focused: comfortable taking a spec from the Technical Program Lead and running with it with minimal hand-holding — but "execution" here means directing and reviewing agentic output, not manual coding for its own sake.
• Fluent in Agile/Scrum — active participant in ceremonies, disciplined about JIRA hygiene and sprint commitments.
• Clear communicator — flags risks/blockers early, documents decisions, and can explain technical trade-offs to the Technical Program Lead and, when needed, the client.
• Mentors junior AI Application Engineers — reviews their code/PRs, helps them write better specs for AI coding agents, and brings them up to speed on RAG/SLM patterns and air-gapped deployment practices.
• Self-driven and self-governed, per Zettabolt's high-ownership hybrid culture.
• Mentor juniors.
• Preferred: background in an IT/consulting services company.

Good to Have:

• Exposure to Big Data tooling (Spark/Hive/Hadoop) or Graph Analytics.
• Experience in a regulated or air-gapped delivery environment (defense, government, BFSI).
• Familiarity with AI governance/evaluation frameworks (guardrails, red-teaming, model cards).
• Contribution to open source projects, academic papers published, filled patents.

AI Lead Apply Now

📍 Location: Noida | Type: Full-Time, Permanent | Experience: 8+ years

We're looking for a hands-on Technical Program Lead to design and deliver enterprise GenAI/SLM solutions, including air-gapped, on-prem, and sovereign deployments. You'll own architecture end-to-end — model selection, infra, deployment, and governance — while leading delivery and the client relationship.

Key Responsibilities:

• Architect GenAI/SLM solutions (RAG, agentic workflows, fine-tuning/distillation) suited to customer security and data-sensitivity constraints.
• Evaluate SLMs vs. LLMs (Phi, Mistral, Llama, Qwen, etc.) on cost, latency, and accuracy trade-offs.
• Design air-gapped/offline deployments — local inference, vector stores, and secure model/data update pipelines with no external dependency.
• Architect across hybrid environments: AWS/Azure/GCP, private cloud, and on-prem data centers, optimizing GPU/CPU cost and performance.
• Define AI governance: model evaluation, guardrails, audit logging, and responsible-AI practices — including offline-compatible monitoring for restricted environments.
• Lead client discovery workshops, translate business requirements into a scoped delivery roadmap, and drive the engagement through to shipment/go-live.
• Own planning and task allocation across the team — break architecture into workstreams, assign to the right engineers, and sequence delivery against client timelines.
• Be the primary point of client interaction throughout the engagement — status updates, scope changes, escalations — not just at kickoff/handoff.
• Drive multiple projects/accounts in parallel, balancing priorities across engagements and flagging capacity or scope risk early.
• Lead a team of engineers/data scientists — planning, reviews, and unblocking delivery.
• Support pre-sales: scoping, estimation, and technical proposals.

Required Skills & Experience:

• 8+ years in software/data engineering, 3+ years architecting production ML/GenAI solutions.
• Hands-on with SLMs/LLMs, fine-tuning (LoRA/QLoRA), quantization; Python, LangChain/LlamaIndex, vLLM/Ollama.
• Proven experience with air-gapped or on-premise AI deployment.
• Cloud architecture (AWS/Azure/GCP) plus hybrid/private data center deployment.
• Vector DBs deployable offline (FAISS, Milvus, Weaviate, Qdrant).
• Familiarity with AI governance/compliance frameworks (NIST AI RMF, ISO/IEC 42001) and data residency requirements.
• Docker/Kubernetes and infra-as-code (Terraform/Ansible).
• Expert in Claude-driven development — using Claude Code and Claude-based agents as a core part of the build workflow, including authoring custom Skills/MCP tools and agentic coding pipelines to boost team engineering productivity.
• Reviewer-first mindset: with agents doing most of the generation, your value is in specifying correctly, critically reviewing AI-generated architecture/code, catching subtle design and security flaws, and validating trade-offs — not in hand-writing every line yourself.

Behavioural & Leadership Expectations:

• Must have: prior experience leading a small team (formally or as a de facto lead) and working across multiple clients/engagements simultaneously — this is not a first team-lead or first multi-client role.
• Leads a team end-to-end; owns the client relationship from requirement gathering through shipment.
• Spends more time planning, allocating, and reviewing than hand-coding — sets direction, defines specs/guardrails for agentic tooling, allocates tasks across the team, and audits output; comfortable being judged on decision quality and delivery outcomes, not lines of code written.
• Able to run multiple projects/accounts simultaneously without losing quality of client interaction on any one of them.
• Fluent in Agile/Scrum ceremonies; hands-on with JIRA/Confluence for backlog and delivery tracking.
• Self-driven, strong client-facing communicator across technical and non-technical stakeholders.
• Preferred: background in an IT/consulting services company over purely captive/product environments.

Good to Have:

• Big Data (Spark/Hive/Hadoop), Graph Analytics, or hardware acceleration (GPU/FPGA) experience.
• Regulated-industry (defense, government, BFSI) AI deployment experience.
• Cloud, security, or AI governance certifications.
• Contribution to open source projects, academic papers published, filled patents.