Staff AI/ML full Stack Engineer & Lead

Role Summary

We are seeking a Staff AI/ML solution lead to lead the architecture, design, and delivery of high-performance, enterprise-grade applications. This role combines deep hands-on coding with high-level architectural decision-making. You will work across frontend, backend, cloud infrastructure, database selection and integration layers, ensuring our systems are secure, scalable, and maintainable while enabling long-term technical growth. This hybrid role combines hands-on software engineering, devops and architectural leadership, enabling the delivery of robust, scalable, and innovative AI systems.

Key Responsibilities:Architecture Leadership Define system architecture, integration patterns, and technology standards for large-scale web and enterprise applications.
Full Stack Development Build and maintain robust, responsive applications using modern frontend frameworks (React, Vue, streamlit or Angular) and backend services in Python, Golang or RUST.
Cloud & Infrastructure Architect cloud-native solutions leveraging AWS with a focus on scalability, security, and performance. Implement containerized services with Docker and orchestrate deployments using Kubernetes (K8s).
API & Service Design Develop RESTful and GraphQL APIs for internal and external integrations.
DevOps & CI/CD Establish best practices for deployment pipelines, automated testing, and infrastructure-as-code (Terraform, Pulumi).
Performance Optimization Drive system performance tuning, load balancing, and efficient code design.
Technical Mentorship Coach and mentor engineers, conduct design/code reviews, and uphold engineering best practices.
Cross-Functional Collaboration Partner with product, design, and business teams to deliver impactful solutions aligned with company objectives.
Databases: Will be performing database selection and deployment (strong devops experience required)
ML: Experience with both ML and LLM stack design (model hubs, vector DBs, embedding pipelines). The role required knowledge to deploy end-to-end architecture of ML applications, traditional and RAG applications, Design of the MLOPS architectures databricks, aws and google
ML ops: Strong understanding of Agentic AI, framework, best practices
Clouds: Databricks, AWS mandatory
End to End production level AI/MLl product deployment experience is required

Qualifications

Must Have:

Required Qualifications:At least bachelor's in Computer Science mandatory
10+ years in deployment enterprise grade cloud level experience and 5+ years in software development
5+ years of experience with Databricks and AWS MLops deployment
This role is more of a software lead and developer with strong Cloud experience to develop infra softwares.
Architect end-to-end agentic pipelines and tools for others to contribute in the team
The role required knowledge to deploy end-to-end architecture of ML applications, traditional and RAG applications.
Architect end-to-end AI/ML systems from data ingestion to model deployment.
Define best practices for model serving, data pipelines, and ML-OPS strategies.
engineering, including hands-on model development and architectural design.
Expertise in traditional ML, deep learning, LLMs, embeddings, and RAG frameworks.
Strong software engineering skills: Python, API development, microservices, database design, and version control (Git).
Experience with cloud platforms (AWS, Databricks, Google) and containerized deployments (Docker, Kubernetes).
Knowledge of ML-OPS, CI/CD for AI, and production model monitoring.
Strong understanding of software architecture patterns, distributed systems, and scalable data pipelines.
Databases: Will be performing database selection and deployment (strong devops experience required)

Preferred:Experience with event-driven architectures and messaging systems (NATs, Kafka, RabbitMQ).
Familiarity with authentication and authorization frameworks (OAuth2, JWT, SSO).
Knowledge of observability and monitoring tools (Prometheus, Grafana, OpenTelemetry).
Background in designing large-scale enterprise or SaaS platforms.
Python, Golang and Rust development experience is preferred
Experience in manufacturing and predictive maintenance is a plus
Background in controls engineering is a plus

Soft SkillsStrong decision-making and problem-solving skills in high-stakes technical environments.
Ability to lead and influence architectural direction across teams.
Excellent communication with both technical and non-technical stakeholders.

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