AI junior Engineer

Role 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. 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.