Senior Technical Architect - Data Science
- Srijan Spectrum
- 14 - 20 years
- Noida, Gurugram
- 5 months ago
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Job Description
We re seeking a hands-on Sr. Data Science Architect who can lead the end-to-end modeling lifecycle from problem framing and experiment design to production deployment and monitoring while setting up the technical architecture for ML/GenAI and agentic systems. This is not a data-engineering-heavy role; you ll partner with DE/Platform teams, but your center of gravity is modeling excellence, MLOps, and AI solution architecture that moves business KPIs.
What you ll do Strategy & Architecture (Data Science first)
Own the technical vision for data-science initiatives; translate ambiguous business goals into modellable problems, KPIs , and NFRs/SLOs .
Define reference architectures for classical ML, deep learning, and agentic GenAI (RAG, tool-use, human-in-the-loop) including model registry, evaluation harness, safety/guardrails, and observability.
Make build vs. buy and model/provider choices (OpenAI/Claude/Gemini vs open-source), including optimization strategies (INT8/4, AWQ/GPTQ, batching, caching).
DS Leadership & Experimentation Lead problem decomposition , feature strategy, experiment design (A/B, interleaving, offline/online eval) , error analysis, and model iteration.
Guide teams across NLP, CV, speech, time series, recommendation, clustering/segmentation , and causal/uplift where relevant.
Establish rigorous quality bars : data & label quality checks, leakage prevention, reproducibility, and statistical validity.
Productionization & MLOps Architect CI/CD for models (unit/contract tests, drift checks, performance gates), model registry/versioning , and safe rollouts (shadow, canary, blue-green).
Design monitoring for accuracy, drift, data integrity, latency, cost, and safety (toxicity, bias, hallucination); close the loop with automated retraining triggers where appropriate.
Orchestrate RAG pipelines (chunking, embeddings, retrieval policies), agent planning/execution , and feedback loops for continuous improvement.
Stakeholders & Enablement Partner with product, strategy/innovation, design, and operations to align roadmaps; run architecture and model review sessions with clear trade-offs.
Provide technical mentorship to data scientists/ML engineers; codify patterns via playbooks, ADRs, and reference repos.
Collaborate with Ops/SRE to ensure solutions are operable : runbooks, SLIs/SLOs, on-call, and cost controls.
Governance, Risk & Compliance Embed model governance : approvals, lineage, audit trails, PII handling, policy-as-code; support GDPR/ISO/SOC2 requirements.
Champion human oversight for agentic systems with clear escalation and decision rights.
Must-have qualifications
14 20 years delivering AI/ML in production, with 5+ years in an architect/tech-lead capacity.
Expert Python and ML stack ( PyTorch and/or TensorFlow ), plus strong SQL and software engineering fundamentals (testing, packaging, profiling).
Proven record architecting scalable DS solutions on AWS/Azure/GCP ; hands-on with Docker and Kubernetes (collaborating with platform teams rather than building infra from scratch).
MLOps proficiency: MLflow/Kubeflow , model registry, pipelines (Airflow / Prefect / Vertex / Bedrock / SageMaker pipelines), feature stores, and real-time/batch serving ( KServe/Seldon/Triton/vLLM/Ray Serve ).
Depth across traditional ML and DL (NLP, CV, speech, time-series, recommendation, clustering/segmentation) and the ability to select/prioritize the right approach for the KPI.
Excellence in communication and stakeholder leadership ; experience guiding cross-functional teams (DS, MLE, DE, Product, Ops) to ship value.
Preferred qualifications Agentic AI & RAG: LangChain/LangGraph or equivalent orchestration; vector DBs ( pgvector , Pinecone, Weaviate, Qdrant); retrieval policy design and evaluation.
Evaluation & Safety: offline metrics (precision/recall, ROC/PR, BERT-F1, BLEU/ROUGE), LLM eval harnesses , red-teaming, prompt/response guardrails.
Experimentation: online testing at scale, counterfactual/causal inference, telemetry design.
Performance & Cost: quantization, speculative decoding, KV caching, batching/collation, throughput tuning on CPU/GPU.
Familiarity with data-viz/decision support (Tableau/Power BI/D3) and UX/HCI collaboration for human-in-the-loop designs.
Consulting experience or multi-vendor delivery; pre-sales/SoW exposure.
Job Classification
Industry: IT Services & ConsultingFunctional Area / Department: Engineering - Software & QARole Category: Software DevelopmentRole: Technical ArchitectEmployement Type: Full timeContact Details:
Company: SrijanLocation(s): Noida, Gurugram+ View Contact
Keyskills: Solution architecture ISO Consulting Packaging Presales Open source Monitoring SQL Python Auditing
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