We are looking for a hands-on Machine Learning Engineer - 1 with a strong understanding of the modern AI stack and the ability to contribute to production-grade AI systems. The ideal candidate understands how LLMs work, can navigate a real codebase, and is comfortable using AI tools to accelerate their work without becoming dependent on them.
You will be embedded directly in our engineering team and contribute to AI pipelines, APIs, and agent systems with guidance from senior ML engineers. Expect meaningful responsibility, thoughtful code reviews, and production exposure from day one.
Role Requirements:
LLM Fundamentals & Prompting
Clear understanding of how LLMs work, including tokenization, context windows, temperature, structured output formatting, LLM guardrails, and hallucination prevention.
- Proficiency in prompting techniques such as few-shot prompting, system prompts, structured outputs, role prompting, and reasoning-oriented prompts.
- Familiarity with recent model releases, capability shifts, and architectural developments.
Ability to run inference on local LLMs using tools like Ollama, VLLM, or Hugging Face Transformers
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AI Systems & Engineering
- Moderate to strong understanding of RAG architecture, including chunking, embeddings, retrieval, reranking, and generation.
- Working knowledge of AI agents, tool use, and agent orchestration frameworks such as LangChain, LlamaIndex, AutoGen, or custom frameworks.
- Understanding of MCP (Model Context Protocol) and AI skills/tool design
- Transformer architecture basics — attention mechanism, encoder/decoder, positional encoding, embeddings
- Good foundation in neural networks, including core concepts such as layers, activation functions, loss functions, backpropagation, optimization, and model evaluation.
Python & Backend Engineering
- Strong programming skills, especially in Python, including clean, idiomatic code, proper error handling, type hints, debugging, and the ability to build efficient algorithms for practical engineering problems.
- Ability to work with structured outputs: JSON schema, Pydantic models, data validation patterns
- Flask API development: REST endpoints, request/response handling, middleware
- Fundamental understanding of API scaling, including async and sync patterns and basic load considerations.
- Expert-level Git operations: branching strategy, PR workflows, commit hygiene, rebasing/merging, resolving conflicts, and maintaining clean collaboration history.
AI Tools & Codebase Navigation
- Ability to independently navigate an existing, non-trivial codebase using AI-assisted tools (Cursor, Claude, ChatGPT, GitHub Copilot, etc.)
- Uses AI tools to boost velocity — but can reason through code independently and does not require AI to explain every line
Computer Vision & Document AI (baseline knowledge required; expertise is not mandatory)
- Rough working knowledge of image processing concepts: preprocessing, transformations, color spaces
- Familiarity with OCR tools and their practical limitations (Tesseract, Docling, AWS Textract, etc.).
- Basic awareness of object detection concepts (bounding boxes, YOLO-style models)
Communication & Collaboration
- Ability to articulate technical decisions clearly in review calls and project syncs without needing repeated prompting to explain reasoning.
- Good ability to think through solution architecture and system design, including component boundaries, data flow, API design, scalability trade-offs, reliability, and maintainability.
Strong written communication for async updates, PRs, and documentation
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Working Proficiency Requirements:
Candidates do not need to be experts in these areas, but they should have enough working knowledge to understand, discuss, and contribute.
- Docker: containerizing Python services, multi-stage builds, docker-compose for local stacks
- Deep RAG expertise: Graph RAG, hybrid retrieval, vector database internals (Pinecone, Weaviate, Qdrant, pgvector)
- Custom LLM agent design: memory management, multi-step reasoning, tool routing, state machines
- Agent observability: tracing, logging agent runs, dashboards (LangSmith, Phoenix, custom)
- LLM/VLM fine-tuning: PEFT methods (LoRA, QLoRA), GRPO, instruction tuning pipelines
- Fine-tuning object detection models for practical use cases, including dataset preparation, annotation quality checks, training configuration, and performance evaluation.
- Good working ability with vision agents, including visual reasoning workflows, image/document inputs, OCR/VLM handoffs, and tool-based orchestration.
- Good understanding of audio handling in agent workflows, including transcription, audio preprocessing basics, speech-to-text integration, and routing audio-derived outputs into downstream tools.
- Good ability to handle multi-extension documents across formats such as PDFs, images, spreadsheets, presentations, text files, and structured exports in AI pipelines.
- Good understanding of converting structured data to unstructured representations and extracting structured outputs from unstructured inputs.
- Awareness of database selection based on use case, including when to use MongoDB, Neo4j, SQL databases, vector databases, or hybrid storage patterns.
- Frontend / UI basics: HTML/CSS/JS or Streamlit for internal tooling and demos
- Ability to actively contribute to product and solution architecture discussions by proposing practical design options and understanding trade-offs.
- Awareness of the latest releases, frameworks, and modern technologies, with the ability to choose current, practical solutions instead of relying on outdated approaches.
