Develop and maintain Python-based services, APIs, and integration layers for customer implementations.
Build data ingestion, transformation, and validation pipelines to support AI/ML workflows.
Implement customer-specific logic, connectors, and automations using Python microservices.
Integrate internal ML pipelines, LLM components, and retrieval systems into customer environments.
Work with RAG pipelines, embedding workflows, or NLP modules as needed for solution deployment.
Collaborate with ML Engineers to productionize models and optimize performance.
Configure and deploy services on cloud platforms (Azure/AWS/GCP).
Ensure reliability, observability, and performance of implementation-specific services.
Troubleshoot production issues, analyze logs, and perform root-cause analysis.
Work closely with Customer Success & Solutions teams to translate requirements into technical specifications.
Own end-to-end technical implementation for enterprise accounts.
Provide guidance on best practices, architecture choices, and scalable patterns.
Participate in code reviews and maintain high-quality coding standards.
Document implementation workflows, integration steps, and troubleshooting playbooks.
Mentor junior team members and contribute to internal tooling and automation.

Total 3+ years of professional experience.
2+ years of Python coding experience.
Strong understanding of backend fundamentals, microservices, and distributed systems.
Experience working with APIs, web frameworks (FastAPI, Flask, Django), and RESTful architectures.
Hands-on experience with relational & NoSQL databases (PostgreSQL, MongoDB, or similar).
Familiarity with cloud platforms (Azure, AWS, or GCP).
Strong problem-solving, debugging, and communication skills.
Ability to work cross-functionally with engineering, ML, and customer-facing teams.
Scope work, sequence delivery, and remove blockers early.
Make trade-offs between scope, speed, and quality; adjust plans to protect delivery.
Contribute directly in the code when progress or clarity depends on it.
Codify working patterns into tools, playbooks, or building blocks that others can use.
Keep teams moving through clarity and follow-through.
Desired (Nice-to-Have)
Experience with AI/ML/NLP pipelines, LLMs, or RAG-based applications.
Good knowledge in Java, NLP or text processing or agentic implementation experience.
Knowledge of Docker, Kubernetes, and container orchestration.
Exposure to message queues (Kafka, RabbitMQ).
Experience with CI/CD tools and automation frameworks.
Prior startup or scale-up experience.
Open-source contributions or technical writing experience.

Customer Implementation Engineer, Python, AI/ML

Key skills