Lead AI Engineer
Job Description
Job Description
Design, build, and scale enterprise-grade AI/GenAI solutions that automate business processes, enhance decision-making, and deliver measurable business outcomes. The role focuses on developing AI-powered applications, intelligent workflows, and integrations by combining enterprise systems, data, APIs, and modern AI technologies in a secure, scalable, and production-ready environment.
Responsibilities
- Design, develop, and deploy AI/GenAI applications, RAG pipelines, and intelligent workflow automation
- Build backend services, APIs, integrations, and orchestration frameworks using LLMs, model APIs, and enterprise data.
- Develop AI-powered knowledge assistants, workflow automation, and decision support solutions.
- Leverage AI-assisted development while ensuring code quality, security, reliability, observability, and governance.
- Define evaluation frameworks to measure AI quality, accuracy, latency, and system performance.
- Collaborate with business, IT, Security, Data, HR, and Finance teams to identify and deliver high-value AI use cases.
- Prototype rapidly and scale solutions into secure, production-ready deployments.
- Optimize AI solutions for performance, scalability, reliability, and cost.
- Develop reusable AI components, frameworks, and best practices to accelerate enterprise adoption.
Required Qualifications
- 7–12+ years of experience in Software Engineering, AI/ML, or GenAI application development.
- Proven experience building and deploying production-grade AI/GenAI solutions.
- Strong expertise in Python, backend development, APIs, system integration, and workflow orchestration.
- Hands-on experience with RAG, LLMs, prompt engineering, tool/agent calling, AI evaluation, and observability.
- Experience integrating enterprise applications, data platforms, and services using APIs and event-driven architectures.
- Strong analytical, systems thinking, and problem-solving skills with the ability to translate ambiguous business problems into scalable AI solutions.
Preferred Qualifications
- Experience with vector databases, embeddings, semantic search, and advanced RAG architectures.
- Knowledge of SQL, data engineering, and data pipelines.
- Experience integrating AI into enterprise applications and business workflows.
- Exposure to intelligent agents, automation platforms, and AI governance frameworks.
- Experience building reusable AI frameworks, accelerators, or enterprise AI platforms.
More Info
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Key Skills
embeddings
data pipelines
LLMs
workflow orchestration
event-driven architectures
vector databases
prompt engineering
RAG
AI evaluation
observability
About Company
Skills:
telemetry , MLops, Docker, Openshift, Python, Kubernetes, Logging, Infrastructure as Code, GitOps, Cloud-native systems, Operational analytics, Observability, Monitoring
Skills:
Gcp, Distributed Systems, Azure, Python, AWS, Embeddings, Scalable AI architectures, Vector databases
Skills:
Backend Development, system integration, Apis, Python, LLMs, workflow orchestration, RAG, event-driven architectures, AI evaluation, observability, prompt engineering
Skills:
data warehouses , data engineering , snowflake , Orchestration, Pyspark, Cloud Technologies, Python, AWS, Sql, Gcp, quality assurance, Spark, Databricks, Azure, Etl, automation use cases, AI engineering, delivery governance, risk management, estimation planning, Data Lakes, data platforms, Lakehouse architectures, model integration, AI ML concepts, large-scale data processing, ELT pipelines, GenAI solutions, stakeholder reporting
Skills:
.NET, .Net Core, , Rest Assured, Sql, SQL Server, Selenium, Rest Apis, Angular, Integration Testing, Software Testing, Postman, Etl Testing, Api Testing, Database Migration, PostgreSQL, GenAI, Playwright, Cypress, Test Automation Frameworks, Karate, end-to-end testing, Agile Delivery Models