About the Role

Automation Lead – AI Engineering & Delivery to lead the design, development, and production delivery of enterprise AI solutions. The role requires strong expertise in Generative AI, Agentic AI, software engineering, cloud-native architecture, and MLOps/LLMOps, complemented by working experience in SRE and AIOps. The ideal candidate combines hands-on technical depth, engineering leadership, and strong delivery ownership, taking AI solutions from concept and PoC to scalable, reliable production services.

Responsibilities

- Lead end-to-end AI/GenAI solution development and delivery, from architecture and PoC through production.
- Design solutions leveraging LLMs, RAG, AI agents, vector search, APIs, and enterprise data integrations.
- Provide technical leadership across architecture, engineering standards, code/design reviews, and reusable AI components.
- Establish MLOps/LLMOps, CI/CD, testing, evaluation, monitoring, and AI lifecycle management practices.
- Ensure AI solutions meet requirements for security, scalability, performance, reliability, observability, and cost efficiency.
- Apply SRE practices including SLIs/SLOs, observability, alerting, incident management, and production-readiness reviews.
- Implement enterprise AI guardrails and validation layers to ensure deterministic, safe, and compliant system responses.
- Drive AI FinOps practices to monitor, forecast, and reduce inference costs across apps.
- Design AI pipelines that strictly adhere to global data protection regulations (e.g., EU AI Act, GDPR, CCPA) by ensuring PII masking, data lineage tracking, and secure enterprise data isolation.
- Leverage AIOps for intelligent monitoring, anomaly detection, event correlation, incident enrichment, and automation.




- Lead and mentor engineers while collaborating with Product, Architecture, Data, Cloud, Security, SRE, and Operations teams.
- Evaluate emerging AI technologies and translate them into practical enterprise use cases with measurable business value.
- Building enterprise AI solutions that move beyond experimentation into secure, scalable, observable, reliable, and business-value-driven production services.

Required Skills

- Required Experience & Skills
- 6+ years in software/technology engineering with experience leading complex technical delivery.
- Strong experience in AI/ML, Generative AI, LLMs, RAG, Agentic AI, Python, APIs, and cloud-native development.
- Proven experience taking AI or digital solutions from PoC to enterprise production.
- Experience with DevOps, MLOps/LLMOps, CI/CD, containers, Kubernetes, and cloud platforms.
- Experience with enterprise vector search engines (e.g., Pinecone, Milvus, Qdrant, PGVector, or Enterprise Elasticsearch).
- Hands-on experience with LLM orchestration tools (e.g., LangChain, LlamaIndex, AutoGen, CrewAI).
- Experience with LLM-specific monitoring tools (e.g., LangSmith, Arize, weights & biases) and traditional APM suites (e.g., Datadog, Prometheus).
- Working/intermediate knowledge of SRE, observability, incident management, and AIOps.
- Strong technical leadership, stakeholder management, problem-solving, and communication skills.
- Ability to lead engineering teams while remaining technically engaged in architecture and critical design decisions.

Preferred Skills

- Positive to have Experience deploying and managing scalable infrastructure within the AWS ecosystem (EKS, ECS, Lambda etc).
- Familiarity with AWS-native AI/ML services such as Amazon Bedrock.

📌 Generative AI Engineer (Bengaluru)
🏢 Andor Tech
📍 Bengaluru