Overview

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.

Responsibilities

• 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.
• ● 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.

Qualifications

• ● 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
• 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.