Senior AI Engineer

Roles & Responsibilities:

Architect scalable AI solutions: Define end-to-end reference architectures (LLM/RAG, NLP, vision, agentic workflows) that move cleanly from DemoBytes → POC → MVP → Demoable → Production.

Own full-stack delivery: Build features across data/ML, backend APIs/services (FastAPI/Flask), and lightweight UIs (React/Next.js) for demoable, user-ready outputs.

Rapid prototyping: Stand up POCs in days; harden validated solutions into MVP and production with incremental quality/security gates.

MLOps & platformization: Implement CI/CD/CT for models, datasets, prompts; automate evals, canary/rollback, versioning, model/data drift monitoring, and experiment tracking (W&B/MLflow).

Integration & interoperability: Embed AI into existing products and workflows via APIs, queues, SDKs, and webhooks with clear SLAs and observability.

Operate what you build: Instrument services, track p95 latency/availability/cost, and drive continuous improvement post-launch.

Mentor & uplift: Coach engineers on best practices (prompting, vector design, evals, latency/cost tuning, secure data handling).

Release cadence: Maintain monthly demo releases and production releases every two months with ALM-driven governance.

Ethical AI & compliance: Apply privacy-by-design, bias testing/mitigation, model cards, auditability, and data protection controls; ensure documentation in ALM.

Trendwatching: Track state-of-the-art AI (models, toolchains, infra) and pragmatically incorporate breakthroughs into roadmaps.

Qualifications:

4–6 years delivering AI/ML features to production with fast POC → MVP → Production cycles.

Strong ML/DL fundamentals; hands-on with PyTorch and/or TensorFlow/Keras; LLMs (prompting, fine-tuning/LoRA), RAG patterns, and evaluation.

Python proficiency; scikit-learn, spaCy/NLTK; Hugging Face (Transformers/Datasets/PEFT); familiarity with YOLO/FastAI (role-relevant).

Backend engineering for production (FastAPI/Flask), auth, caching, testing; practical React/Next.js for demoable UIs.

MLOps: Docker/Kubernetes, CI/CD (GitHub Actions/Azure DevOps/Jenkins), experiment tracking (Weights & Biases/MLflow), monitoring (Prometheus/Grafana/OpenTelemetry).

Data & storage: SQL/NoSQL (Postgres, Redis), object stores; vector DBs (FAISS/Milvus/pgvector) and retrieval design.

Cloud: AWS/Azure/GCP with cost/latency/performance trade-off literacy.

AI productivity tools (required): Cursor, Windsurf, Claude, Copilot for accelerated prototyping, code gen/review, and prompt workflows.

Effective communication; crisp documentation and governance in ALM.

Working knowledge of ethical AI and data protection (PII handling, access controls, audit trails).

Job Type: Full-time

Experience:

AI: 4 years (Required)

Python: 4 years (Required)

Work Location: In person