AI/ML Engineer
Primary Skills
Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio
Specialization
Data Science Advanced: Data Specialist
Job requirements
AI Engineer – Agentic AI Platforms & Applications
About the Role
We are looking for highly motivated AI Engineers to design, build, and deploy next-generation AI agents and autonomous workflows that solve real business problems. You will work closely with product, operations, and business teams to create production-grade agentic applications powered by LLMs, enterprise data, and modern AI orchestration frameworks. This role is ideal for engineers who enjoy rapid experimentation, solving ambiguous problems, and turning AI prototypes into scalable enterprise solutions.
What You’ll Do
Design, build, and deploy AI agents and multi-agent systems using modern LLM frameworks and enterprise AI platforms
Develop agentic workflows for business functions such as Finance, Legal, Operations, Sales, Support, and Growth
Build production-ready applications using LLMs, RAG pipelines, tool calling, memory systems, and orchestration frameworks
Integrate AI agents with enterprise platforms such as Google Workspace, Slack, CRM systems, internal APIs, databases, and knowledge repositories
Evaluate and leverage foundation models across providers (Gemini, OpenAI, Anthropic, open-source models, etc.) based on use case requirements
Work closely with business stakeholders to identify opportunities, prototype solutions rapidly, and iterate based on user feedback
Create reusable agent frameworks, prompt libraries, evaluation pipelines, and deployment patterns
Implement observability, guardrails, evaluation, and monitoring for AI applications in production
Optimize agent performance for latency, accuracy, reliability, and cost
Contribute to internal best practices around agent architecture, prompting, RAG, and AI engineering standards
Stay current with emerging trends in autonomous agents, AI infrastructure, and enterprise AI adoption What We’re Looking For
Strong software engineering fundamentals with experience building scalable backend or full-stack applications
Hands-on experience with LLMs and modern AI application development
Experience building AI agents, autonomous workflows, or agentic applications
Familiarity with frameworks such as LangChain, LangGraph, CrewAI, Google ADK, AutoGen, Semantic Kernel, or similar
Strong understanding of: o RAG architectures o Prompt engineering o Vector databases o Tool/function calling o AI workflow orchestration o Context and memory management
Experience working with cloud platforms such as Google Cloud, AWS, or Azure
Experience with Vertex AI, Gemini Enterprise, OpenAI APIs, or similar enterprise AI platforms is a strong plus
Familiarity with APIs, microservices, event-driven systems, and enterprise integrations
Comfortable working in ambiguous environments with evolving requirements and rapid experimentation cycles
Strong communication skills and ability to collaborate with both technical and non-technical stakeholders
Builder mindset with strong ownership and execution capabilities
Preferred Qualifications
Experience deploying AI applications into production environments
Familiarity with AI evaluation frameworks, observability, and guardrails
Experience with Google Workspace APIs, Slack integrations, or enterprise automation tools
Knowledge of fine-tuning, model optimization, or open-source LLM deployment
Exposure to multi-agent coordination and autonomous decision-making systems
Experience working in fast-paced startup or innovation environments
Experience
4–8 years of software engineering experience
2+ years of hands-on experience building AI/LLM-powered applications preferred Nice to Have
Experience with Python-based AI ecosystems
Knowledge of vector databases such as Pinecone, Weaviate, Chroma, or Vertex AI Vector Search
Experience with Kubernetes, Docker, CI/CD, and cloud-native deployments
Contributions to open-source AI projects or experimentation with emerging agentic frameworks
