Senior Data Science Architect

Senior Technical Architect — Data Science & Agentic AI

Reports to:

Employment Type:

About us

We turn customer challenges into growth opportunities.

Material is a global strategy partner to the world’s most recognizable brands and innovative companies. Our people around the globe thrive by helping organizations design and deliver rewarding customer experiences.

We use deep human insights, design innovation and data to create experiences powered by modern technology. Our approaches speed engagement and growth for the companies we work with and transform relationships between businesses and the people they serve.

Srijan, a Material company, is a renowned global digital engineering firm with a reputation for solving complex technology problems using their deep technology expertise and leveraging strategic partnerships with top-tier technology partners.

Role summary

• 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 cho ices (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.

Kindly apply or share your resume with me at Vineet.kumar@materialplus.io