The Role
Designs and scales Python backend services and APIs while building production agentic AI, RAG, LLM, data, and ML systems. Deploys cloud-native services, establishes evaluation and observability practices, leads technical design, mentors engineers, presents solutions to clients and leadership, and supports production incidents.
Summary Generated by Built In
- Design, build, and scale backend services and APIs using Python with FastAPI, Django, and Flask
- Build and ship agentic AI systems: multi-step agents, tool calling, MCP integrations, and orchestration with frameworks like LangChain/LangGraph, LlamaIndex, CrewAI, or the Claude Agent SDK
- Develop RAG pipelines, including document ingestion, chunking, embeddings, vector databases, retrieval tuning, and evaluation
- Integrate and optimize LLMs (OpenAI, Anthropic Claude, Gemini, open-source models) for accuracy, latency, and cost
- Build data pipelines, analytics, and ML workflows using Pandas, NumPy, scikit-learn, and PyTorch or TensorFlow
- Set up LLM evaluation, guardrails, monitoring, and observability (LangSmith, Langfuse, or similar) for production AI systems
- Deploy and operate services on AWS, GCP, or Azure with Docker, Kubernetes, and CI/CD pipelines
- Lead technical design, break down work, mentor engineers, and set code-quality and AI-engineering standards
- Present AI solutions, demos, and technical roadmaps to clients, stakeholders, and leadership
- Take part in on-call rotations and production incident response
Requirements
- 3 to 6 years of professional software engineering experience, mostly in Python
- Strong production experience with FastAPI plus Django and/or Flask, including async programming and REST API design
- Hands-on experience building LLM-powered applications, including at least one RAG or agent-based system in production
- Working knowledge of vector databases (Pinecone, Weaviate, Qdrant, pgvector) and embedding models
- Solid data science fundamentals: data wrangling, statistics, model evaluation, and working with structured and unstructured data
- Strong SQL skills (PostgreSQL) and experience with NoSQL stores and Redis
- Hands-on cloud experience (AWS, GCP, or Azure) and containerization with Docker
- Strong testing habits (pytest) and clean, typed Python (type hints, Pydantic)
- Understanding of API security, OAuth 2.0, JWT, and responsible handling of sensitive data
- Excellent communication skills, with the ability to explain complex AI concepts simply, run client demos, and confidently represent and sell our technical approach
- Proven ability to lead projects and guide team members from proof of concept to production
- Curiosity and the ability to evaluate and adopt new AI tools and models quickly
Nice to have - Experience with fine-tuning, prompt optimization, or model distillation
- MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI, Bedrock)
- Experience building MCP servers or multi-agent systems
- Big data or streaming tools (Spark, Airflow, Kafka, dbt)
- Familiarity with Node.js or TypeScript for full-stack collaboration
- Published projects, research, Kaggle work, or open-source contributions in AI/ML
- AWS, GCP, or Azure AI or ML certifications
Skills Required
- 3 to 6 years of professional software engineering experience, primarily in Python
- Production experience with FastAPI plus Django and/or Flask, including asynchronous programming and REST API design
- Experience building LLM-powered applications, including a production RAG or agent-based system
- Working knowledge of vector databases and embedding models
- Data science fundamentals, including data wrangling, statistics, model evaluation, and structured and unstructured data
- Strong SQL skills with PostgreSQL and experience with NoSQL stores and Redis
- Cloud experience with AWS, GCP, or Azure and containerization with Docker
- Strong testing practices using pytest and clean, typed Python with type hints and Pydantic
- Understanding of API security, OAuth 2.0, JWT, and responsible handling of sensitive data
- Excellent communication skills and ability to explain AI concepts, run client demos, and represent the technical approach
- Ability to lead projects and guide team members from proof of concept through production
- Curiosity and ability to evaluate and adopt new AI tools and models quickly
- Experience with fine-tuning, prompt optimization, or model distillation
- Experience with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Bedrock
- Experience building MCP servers or multi-agent systems
- Experience with big data or streaming tools such as Spark, Airflow, Kafka, or dbt
- Familiarity with Node.js or TypeScript for full-stack collaboration
- Published projects, research, Kaggle work, or open-source AI/ML contributions
- AWS, GCP, or Azure AI/ML certifications
Devsinc Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Devsinc and has not been reviewed or approved by Devsinc.
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Healthcare Strength — OPD and IPD medical coverage, along with health insurance mentions, indicate a meaningful healthcare offering.
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Flexible Benefits — Work-from-home options, workation opportunities, and paid leave point to flexible arrangements for where and when work is done.
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Wellbeing & Lifestyle Benefits — Access to gyms and salons, plus various allowances and occasional trips or events, add lifestyle value beyond base pay.
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The Company
What We Do
We integrate global leaders in web development with passionate Asian talent to get a unique blend of Quality and Affordability. We are headquartered in California and work consistent eastern and pacific standard hours. We like ad hoc pairing as necessary, TDD, and working with other agencies to make things happen. We contribute to open source projects and genuinely enjoy coding. We are also committed to teaching, and spreading knowledge!
