Overview

Junior AI Application Engineer — GenAI/SLM Applications

Responsibilities

• We are 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 will 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 will 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.