Senior AI Full-Stack Developer

6 years

0 Lacs

Posted:3 days ago| Platform: Linkedin logo

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Work Mode

On-site

Job Type

Full Time

Job Description

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).

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