Job Description: AI/ML Architect
(8+ Experience)
Position Overview
We are seeking an experienced and visionary AI/ML Architect with 8+ years of hands-on expertise in AI/ML systems design, end-to-end product architecture, LLM development, agentic AI frameworks, and large-scale data engineering. The ideal candidate should have deep experience designing, architecting, and implementing advanced AI systems across cloud, big data, and enterprise security platforms.
You will lead the architecture and development of AI and Agentic systems across enterprise products, including cybersecurity analytics, predictive threat modeling, continuous compliance automation, autonomous agents, and intelligence-driven automation platforms.
Key Responsibilities
- AI/ML & LLM Architecture Ownership
- Architect end-to-end AI/ML pipelines including data ingestion, transformation, model development, deployment, monitoring, and optimization.
- Design scalable solutions using ML, deep learning, NLP, LLMs, embeddings, RAG, and agentic AI frameworks.
- Lead LLM finetuning, distillation, optimization, and inference acceleration initiatives.
- Architect generative AI and multi-agent autonomous systems using LangChain, LangGraph, CrewAI, or custom frameworks.
- Agentic AI & Autonomous Systems
- Design multi-agent ecosystems for reasoning, decisioning, investigation, SOC automation, and compliance workflows.
- Architect RAG pipelines using vector databases (FAISS, Pinecone, Qdrant, Elastic, Chroma).
- Implement scalable and deterministic agent orchestration for enterprise workloads.
- Integrate AI-driven automation into SOC, IR, compliance, and auditing systems.
- Data Engineering & Big Data Architecture
- Architect data pipelines using Kafka, Spark, Airflow, Flink, Hadoop, Trino, Iceberg, or Delta Lake.
- Handle structured and unstructured datasets including logs, documents, PDFs, and images.
- Ensure data quality, lineage, governance, and high availability.
- Optimize ETL/ELT workflows, feature stores, and large-scale log ingestion
systems.
- Cloud & MLOps
- Design AI architecture on AWS, GCP, or Azure (preferably AWS SageMaker, Bedrock, ECS/EKS).
- Implement CI/CD-driven model deployment using Docker, Kubernetes, GitOps.
- Establish MLOps frameworks including MLflow, SageMaker Pipelines, and Kubeflow.
- Drive observability, drift detection, and monitoring for production AI systems.
- Leadership & Technical Oversight
- Mentor and guide AI/ML engineers across levels.
- Conduct architecture reviews and ensure best practices.
- Work closely with product, engineering, and leadership teams on AI strategy.
- Ensure compliance with quality, security, and Responsible AI standards.
- Research & Innovation
- Evaluate new LLMs, transformer architectures, and inference engines.
- Build POCs for high-impact AI capabilities.
- Drive innovation in cybersecurity analytics, threat prediction, SOC modernization, and compliance automation.
- Publish internal architecture guidelines and best practices.
Technical Skills
Required Skills & Qualifications
- Expert-level experience in ML, deep learning, NLP, LLMs, and transformer models.
- Strong experience with LLM finetuning, quantization, RAG, embeddings, vector DBs.
- Proficiency in Python. Additional knowledge of Node is a plus.
- Expertise in distributed data systems: Kafka, Spark, Trino, Flink, Iceberg/Delta.
- Strong cloud architecture experience (AWS preferred).
- Experience with microservices, REST/GRPC, and event-driven architecture.
Architecture & Design Skills
- Experience designing scalable AI/ML pipelines and agentic AI ecosystems.
- Capability to create HLD, LLD, data flow, sequence diagrams, and deployment architectures.
- Expertise in security-driven and compliance-driven AI platform engineering.
Soft Skills
- Strong leadership and mentoring ability.
- Excellent communication and stakeholder management skills.
- Strong analytical and high-level problem-solving abilities.
- Ability to drive large-scale initiatives end to end.
Preferred Qualifications
- Experience in cybersecurity analytics, threat detection, UEBA, SOAR, SIEM, MXDR.
- Experience in compliance automation (PCI DSS, ISO, HIPAA).
- Knowledge of GPU optimization, inference servers (Triton, vLLM, TGI).
- Experience designing autonomous agents for SOC, IR, Compliance, or DevSecOps automation.
