Environment : Product & R&D
Location : Remote from India 

Roles and Responsibilities: 

1. Design, build, and maintain scalable data pipelines to collect, clean, transform, and validate structured & unstructured data from multiple internal and external sources.
2. Collaborate with data engineers, cybersecurity teams, AI/ML & LLM/Agent teams, and business stakeholders to define data needs and ensure data accuracy, security, and reliability.
3. Analyze cybersecurity datasets including SIEM logs, CVEs, vulnerabilities, and asset inventories to identify trends, anomalies, and actionable insights that strengthen security analytics.
4. Develop and maintain data models, semantic layers, ontology relationships, lineage documentation, and metadata standards to support governance, interoperability, and transparency.
5. Automate data workflows and scheduling using Airflow, Prefect, or Temporal; support CI/CD processes for data including validation, testing, and version control.
6. Integrate and manage datasets across OCI and hybrid environments, ensuring secure connectivity via VPNs, private endpoints, and cloud storage services.
7. Build and maintain dashboards and BI insights using Power BI, Looker, or Grafana to enable data-driven decisions for leadership and technical teams.
8. Support GenAI/LLM initiatives by preparing datasets, curating data pipelines, performing feature engineering, and enriching metadata to enable high-quality model training.
9. Contribute to AI-driven analytics (AI for BI) by integrating LLMs and Agent-based workflows with BI tools (Power BI, Looker, Tableau) to automate reporting and deliver deeper insights.
10. Continuously improve data quality, documentation, and analytical processes through performance optimization, stakeholder feedback, and best-practice implementation.

Technical Requirements : 

1. 5+ years of experience in SQL & Python (Pandas / PySpark) for data querying, analysis, transformation, and automation.
2. Should have experience working with an AI team and should know how the data are prepared for GenAI solution.
3. Should be able to perform end-to-end feature engineering,  including extraction, selection, transformation, and enrichment of raw data  to make datasets suitable for training ML/GenAI models.
4. Strong hands-on experience with ETL & orchestration tools such as Airflow, Prefect, or Temporal to build robust data workflows.
5. Expertise in data modeling, cleaning, transformation, and handling structured/unstructured formats, including JSON schema understanding.
6. Good understanding of cybersecurity datasets such as SIEM logs, CVEs, vulnerability & asset inventory data.
7. Experience with OCI or similar cloud platforms, including Object Storage, Autonomous DB, Logging, hybrid integrations via VPN & private endpoints.
8. Knowledge of semantic modeling & ontology-based data relationships, metadata management, and lineage tracking.
9. Experience working in AI-enabled analytics environments (e.g., AI for BI), integrating GenAI/LLM-based Agents with BI tools such as Power BI, Looker, or Tableau to automate insight generation, reporting workflows, and data-driven decision support.
10. Hands-on with visualization tools (Power BI / Looker / Grafana), delivering performance dashboards and insights.
11. Experience working on at least one production-grade GenAI/LLM solution (e.g., RAG, AI Agents, automated workflows) and collaboration with AI teams for data readiness.
12. Familiarity with CI/CD for data pipelines, including testing, validation, and version control tools (e.g., Git, DVC), and experience with Agentic workflows.

Environment : Product & R&D
Location : Remote from India 

Roles and Responsibilities: 

1. Design and implement agentic AI systems using frameworks such as LangChain, CrewAI, Autogen, and LlamaIndex for multi-agent reasoning, task orchestration, and automation.
2. Build, optimize, and maintain RAG (Retrieval-Augmented Generation) pipelines for contextual enterprise intelligence and correlation across structured/unstructured data.
3. Deploy, manage, and scale LLM/SLM models in enterprise on-prem or hybrid environments using vLLM, Triton, Kubernetes.
4. Perform model training, fine-tuning, and evaluation of Transformer-based models for enterprise & cybersecurity use cases.
5. Develop and maintain vector database integrations (FAISS, Milvus, Pinecone, etc.) for embedding-based search and retrieval.
6. Build end-to-end agentic orchestration workflows using Temporal or similar orchestration frameworks.
7. Integrate GenAI systems with cybersecurity workflows e.g., threat detection, incident triage, vulnerability scoring, risk analytics.
8. Work with CodeAct/ReACT patterns and MCP-based connectors for contextual agent reasoning.
9. Collaborate with MLOps/DevOps teams to build CI/CD workflows for ML using Docker, Helm, ArgoCD, GitHub Actions.
10. Implement secure AI pipelines with access control, auditing, and policy enforcement.
11. Monitor and optimize production models, including quantization (INT4/8), latency tuning, and hardware utilization.
12. Contribute to infra automation using Terraform & Helm charts for reproducible deployments.
13. Work closely with product, security, and data engineering teams to convert research prototypes into reliable production-grade AI systems.
14. Understand & coordinate data ingestion and ETL workflows; work collaboratively with data engineering teams.
15. Build reusable accelerators, POCs, research pipelines, and scale them to production.

Required Skills & Experience

1. Overall 8+ years experience and minimum 2.5+ years hands-on experience building and deploying GenAI solutions in production.
2. Strong programming experience in Python, PyTorch, and Transformer architectures; hands-on with HuggingFace libraries.
3. Should have proven experience in  building multi-agent AI applications using CrewAI, LangChain, LlamaIndex, or Autogen.
4. Proven experience in deploying LLMs/SLMs in on-prem / hybrid setups using vLLM, Triton.
5. Strong background in RAG pipelines, embeddings, vector DBs (FAISS/Milvus/Pinecone), and context retrieval.
6. Experience performing fine-tuning / supervised training on LLMs/SLMs with domain data.
7. Familiarity with MCP connectors, ReACT/CodeAct frameworks for reasoning.
8. Hands-on MLOps/DevOps: Docker, Helm, GitHub Actions, ArgoCD, Terraform.
9. Experience implementing CI/CD for ML including validation, deployment & rollback.
10. Understanding of ETL concepts & data modeling; ability to collaborate with data engineering teams.
11. Experience contributing to production-ready agentic AI workflows using orchestration frameworks (Temporal preferred).
12. Exposure to cybersecurity AI workflows (optional but preferred).
13. Experience in quantization (INT4/INT8) & performance optimization.