Lead Ai Engineer </h2 >
📝𝐓𝐡𝐞 𝐒𝐡𝐚𝐩𝐞 𝐨𝐟 𝐭𝐡𝐢𝐬 𝐑𝐨𝐥𝐞:
A player-coach role, combining hands-on engineering with technical leadership: - ~70% Building: Stay hands-on, take ownership of the hardest or least-defined problems, write production-grade code, and ship solutions. We are looking for someone who continues to code and build. - ~30% Leading: Serve as the technical lead for a team of 4–5 engineers, driving architecture decisions, design and code reviews, breaking down ambiguous problems, unblocking team members, and raising the team's standards in ML rigour and engineering discipline.
📝𝐊𝐞𝐲 𝐑𝐞𝐪𝐮𝐢𝐫𝐞𝐦𝐞𝐧𝐭𝐬:
ML/DL Fundamentals — From First Principles
- Linear algebra, probability and optimisation as they show up in training: gradients, loss landscapes, regularisation, why a run diverges.
- Classical ML and when it beats a neural network. Feature engineering, leakage, class imbalance.
- Deep learning: backpropagation, CNNs/RNNs, and transformers — attention, tokenisation, embeddings, context windows — at a mechanism level.
- Evaluation discipline. Split design, metric choice and its failure modes, overfitting diagnosis, the offline/online gap, significance on small samples. This is the single thing we probe most.
- Data intuition: you look at the
AI Engineering — Production Judgement
- LLM applications in production: RAG (chunking, embeddings, vector search, reranking), structured output, tool calling, agentic workflows.
- Prompts as engineering artifacts — versioned, tested, measured. Not tuned by vibes.
- Fine-tuning (LoRA/PEFT, instruction tuning) and the judgement to know when it isn't worth it.
- Serving and optimisation: batching, quantisation, streaming, caching, provider fallbacks.
- Cost and latency ownership. You know what a feature costs at scale and how to halve it.
- Software Engineering — this carries equal weight
An AI feature is 20% model and 80% the system around it. We will interview this as seriously as the ML. Python, at depth-
- Production-shaped code: type hints, tested, reviewed, packaged. Not notebook-shaped.
- Async/await and concurrency — you know when it helps, when it doesn't, and what blocks the event loop.
- Comfortable profiling and fixing slow code rather than guessing at it.
- FastAPI (or equivalent) in production, including dependency injection, validation with Pydantic, and background tasks.
REST API Design
- Sensible resource modelling, HTTP semantics and status codes used correctly.
- Versioning, pagination, filtering, and a consistent error contract clients can actually handle.
- Idempotency, retries and timeouts — especially in front of slow, flaky, expensive model calls.
- Authentication and authorisation (JWT/OAuth2), rate limiting, and per-tenant quota enforcement.
- Streaming responses (SSE/WebSocket) for token-by-token output.
- Documented interfaces — OpenAPI, kept honest.
Databases & Data Systems
- Strong relational fundamentals in PostgreSQL: schema design, normalisation and when to denormalise deliberately.
- Indexing you can justify — you read query plans (EXPLAIN ANALYZE) rather than adding indexes hopefully.
- Transactions, isolation levels, and where race conditions actually come from.
- Finding and fixing N+1 queries, and knowing what your ORM is doing underneath.
- Migrations on a live database without downtime.
- Connection pooling and behavior under concurrent load.
- Multi-tenant data modelling and row-level access control.
- A vector store for embeddings, and Redis for caching and queues — with a clear view of what belongs in each.
Running AI Systems in Production
- Docker, CI/CD, cloud (AWS or equivalent).
- Logging, tracing and alerting designed for AI systems specifically — where non-determinism means "it didn't crash" is not the same as "it worked".
📝𝐊𝐞𝐲 𝐒𝐤𝐢𝐥𝐥𝐬 & 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩:
- 5+ years of engineering experience, including 2–3+ years of hands-on ML/DL or AI systems experience in production
- Previous experience leading a small engineering team as a Tech Lead, Staff Engineer, or de facto senior engineer
- Ability to conduct code and design reviews that help engineers improve and grow Ability to turn vague problem statements into clear, actionable, and scoped work
- Strong written communication skills for design documents, evaluation reports, and technical explanations
- Ability to communicate technical concepts clearly to both technical and non-technical stakeholder
- Comfortable communicating uncertainty honestly, including when the answer is “we don't know yet”
- Strong mentoring mindset without gatekeeping knowledge or ownership
📝𝐍𝐢𝐜𝐞 𝐓𝐨 𝐇𝐚𝐯𝐞:
- Document AI, OCR, or handwriting recognition
- Bangla or low-resource multilingual NLP
- Time-series forecasting in a business setting
- ERP or enterprise systems experience, including SAP, Odoo, or custom platforms
- Education domain, including assessment, learning science, or knowledge tracing
- On-device or edge inference
- Open-source contributions or public technical writing
- Internal tools for annotation and AI evaluation
📝 𝗪𝗵𝗮𝘁 𝗪𝗲 𝗢𝗳𝗳𝗲𝗿:
- Competitive salary based on experience and expertise
- Annual performance-based increment
- 2 Festival Bonuses annually
- 3-month probationary period
- 2-day weekend (Friday & Saturday)
- 5 working days per week, 8.5 hours per day
- Fully subsidized lunch and snacks
- Opportunity to work on high-impact AI projects using modern technologies
- Friendly and collaborative team environment
- Learning and growth opportunities with exposure to emerging AI technologies
📝 𝗛𝗼𝘄 𝘁𝗼 𝗔𝗽𝗽𝗹𝘆:
📄 Submit your Resume here 👉🏻 https://forms.gle/px5fEZ8iRMCiVNEy9
📝 𝗗𝗲𝗮𝗱𝗹𝗶𝗻𝗲:
Apply now! Applications will be reviewed on a first-come, first-served basis. Don’t miss the opportunity to join our innovative and dynamic team and work on high-impact AI systems! Apply Now
Location
Gulshan 2, Dhaka
Job Type
Full-time (on-Site)
Deadline
First-come, First-served basis
