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
reusable AI frameworks
data pipelines
LLMs
AI governance frameworks
workflow orchestration
event-driven architectures
vector databases
prompt engineering
automation platforms
enterprise AI platforms
RAG
AI evaluation
observability
intelligent agents
About Company
Skills:
state management , Machine Learning, Docker, AWS, Deep Learning, Python Programming, multi-agent systems, knowledge graph design, AI workflows, DeepEval, RAGAS, vector databases, LLM systems, LangSmith, observability tools, memory systems, API-driven systems, ChromaDB, retrieval pipelines, scalable AI architectures, PGVector, prompt engineering, microservices architecture, Milvus, LangChain ecosystem, Agentic RAG systems
Skills:
Tensorflow, Pytorch, Api Development, Python, ETL and Data Pipelines, Model Deployment, Scikit-learn, Prompt Engineering
Skills:
data engineering , data warehouses , snowflake , Spark, Databricks, Sql, Pyspark, Etl, Cloud Technologies, Orchestration, Gcp, quality assurance, AWS, Python, Azure, risk management, automation use cases, model integration, GenAI solutions, stakeholder reporting, delivery governance, ELT pipelines, Lakehouse architectures, estimation planning, AI ML concepts, Data Lakes, data platforms, large-scale data processing, AI engineering
Skills:
Backend Development, Apis, system integration, Python, LLMs, workflow orchestration, RAG, event-driven architectures, AI evaluation, observability, prompt engineering
Skills:
Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), Automated Testing, Microservices, Angular, Django, React, Typescript, DevSecOps, Docker, Rest Apis, Kubernetes, AWS, embeddings, event-driven architectures, vector databases, CI/CD pipelines, Python FastAPI, Java Spring Boot, observability