AI Architect - AWS ML Solution

Bandhan Technologies; Bengaluru; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Bandhan Technologies in Bengaluru seeks an AI Architect – AWS ML Solution (Hands-On) to design, build, lead, and deliver high-impact AI solutions. Own execution excellence with measurable business value, technical depth, and governance.

Responsibilities include leading design reviews, building automated pipelines, and delivering production-grade solutions for 1M+ users with robust observability and security. Requires 10–12 years of experience and deep AWS/MLOps expertise.

Qualifications

• Bachelor’s or Master’s in CS/CE/EE or equivalent.
• Strong Python and ML tooling experience.
• Proven track record shipping production ML systems.

Responsibilities

• Translate business problems into technical specifications and architectures.
• Lead design reviews, prototype quickly, and harden solutions for scale.
• Build automated pipelines (CI/CD) and model governance across environments.
• Define & track KPIs: accuracy, latency, cost, adoption, compliance.
• Partner with Product, Security, Compliance, and Ops to land secure systems.

Skills

AWS SageMaker

Kubernetes

Docker

MLflow

Kubeflow Pipelines

CI/CD

GitHub Actions

LangChain

CrewAI

Python

Education

Bachelor’s/Master’s in CS/CE/EE/Data Science or equivalent

Tools

SageMaker

Kubeflow

Databricks Feature Store

TensorFlow Serving

TorchServe

FastAPI

OpenSearch

Prometheus

Job description

AI Architect – AWS ML Solution (Hands-On)

• Experience: 10–12 years
• Primary Tech/Domain: AWS
• Must have strong platform engineering skills.

Role Summary

Design, build, lead, and deliver high-impact AI solutions. Own execution excellence with measurable business value, technical depth, and governance.

Key Outcomes (06–12 months)

• Ship production-grade solutions with clear ROI, reliability (SLOs), and security.
• Establish engineering standards, pipelines, and observability for repeatable delivery.
• Mentor talent; uplift team capability through reviews, playbooks, and hands‑on guidance.
• Needs to build a successful AI platform that can be used to build AI applications across different domains

Responsibilities

• Translate business problems into well‑posed technical specifications and architectures.
• Lead design reviews, prototype quickly, and harden solutions for scale (1M+ users / high QPS).
• Build automated pipelines (CI/CD) and model/data governance across environments.
• Define & track KPIs: accuracy/latency/cost, adoption, and compliance readiness.
• Partner with Product, Security, Compliance, and Ops to land safe‑by‑default systems.
• Must have built an AI platform for AI applications across different domains.

Technical Skills: (Most of the levels mentioned below, but must have agentic AI skills)

• Platform: AWS SageMaker,; Kubernetes + Docker for portable inference
• MLOps: MLflow registry, Kubeflow Pipelines, CI/CD (GitHub Actions/Jenkins), canary/champion-challenger
• Monitoring: SageMaker Model Monitor (data/quality drift), monitoring, CloudWatch/Prometheus
• Serving: TensorFlow Serving, TorchServe, FastAPI/GRPC, autoscaling, low‑latency optimizations
• Feature stores & data: Feast, Databricks Feature Store; batch vs. streaming pipelines
• Should have a solid practical knowledge of Machine learning and statistics.
• Security: secret management, network isolation, encryption‑at‑rest/in‑transit, compliance logging
• AWS Bedrock & LLM Integration – Deploy and customize foundation models (GPT, Claude, Titan) using Amazon Bedrock, including prompt engineering and fine‑tuning strategies.
• RAG Architecture with AWS Services – Implement Retrieval‑Augmented Generation using Amazon Kendra for semantic search and OpenSearch for vector embeddings.
• Agentic AI Orchestration – Design multi‑agent workflows leveraging AWS Lambda, Step Functions, and integration with LangChain/CrewAI for event‑driven orchestration.
• MLOps & Deployment on AWS – Build pipelines with SageMaker Pipelines, manage model registry, and enable CI/CD using CodePipeline and CodeBuild.
• Security & Compliance on AWS – Apply AWS IAM, KMS, and Secrets Manager for secure access; ensure compliance with GDPR, HIPAA, and SOC using AWS governance frameworks.
• Design agentic systems using AWS Lambda and Step Functions for orchestration and state management.
• Integrate multi‑agent workflows with Amazon EventBridge for event‑driven architectures.
• Secure agent operations using AWS IAM, KMS, and Secrets Manager for identity and encryption.
• Connect agents to Amazon Comprehend, Polly, and Lex for NLP and conversational capabilities.
• Implement observability with CloudWatch, X‑Ray, and distributed tracing for agent workflows.
• Must have designed and built at least 3 Agentic AI solutions in Azure.
• Must have platform engineering experience.
• Source control & workflow: Git, branching standards, PR reviews, trunk‑based delivery.
• Observability: logs, metrics, traces; dashboards with alerting & on‑call runbooks.
• Data/Model registries: metadata, lineage, versioning; staged promotions.

Performance & Reliability

• Define SLAs/SLOs for accuracy, tail latency , throughput, and availability.
• Capacity planning with autoscaling; load tests; cache design; graceful degradation.
• Cost controls: instance sizing, spot/reserved strategies, storage tiering.

Security & Compliance

• IAM, network isolation, encryption (KMS), secret rotation.
• Threat modeling, dependency scanning, SBOM, supply‑chain security.
• Domain‑regulatory controls (PCI DSS, HIPAA) where applicable; audit readiness.

Qualifications

• Bachelor’s/Master’s in CS/CE/EE/Data Science or equivalent practical experience.
• Strong applied programming in Python; familiarity with modern data/ML ecosystems.
• Proven track record of shipping and operating systems in production.