Principal Software Engineer; Python

Job Description

• 7-10+ years of proven experience as a Principal Software Engineer or similar role.
• Strong proficiency in Python programming language.
• Strong proficiency in database technology (SQL, No
  
  SQL).
• Extensive experience with Azure/AWS cloud services (e.g., EC2, S3, Lambda, RDS).
• Solid understanding of software architecture and design patterns.
• Proven experience designing end-to-end system architectures for complex, distributed applications.
• Experience with containerization and orchestration tools (e.g., Docker, Kubernetes, Terraform).
• Knowledge of CI/CD pipelines and Dev Ops practices.
• Experience working on client-facing or consulting engagements, translating ambiguous business requirements into technical architectures.
• Excellent problem-solving skills and attention to detail.
• Strong communication and leadership abilities.
• Ability to work effectively in a fast-paced, collaborative environment.
• Hands-on experience building AI-enabled applications, including LLM integrations and generative AI use cases.
• Experience designing and implementing RAG architectures, including document ingestion, embedding pipelines, and retrieval optimization.
• Familiarity with vector databases and semantic search technologies (e.g., Solr, Qdrant, Open Search, Elasticsearch).
• Experience with agent-based systems, including tool orchestration, multi-step workflows, and API integrations.
• Understanding of prompt engineering, evaluation frameworks, and techniques to improve model reliability (e.g., hallucination mitigation).
• Experience integrating AI systems into production environments, including monitoring, observability, and cost/performance trade-offs.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances.

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• Experience delivering AI/LLM solutions from prototype to production in enterprise environments.
• Experience with ML pipelines: model deployment, model serving, and debugging.
• Experience with other programming languages (e.g., Java, Go, C++).
• Experience with web frameworks (Django, Flask, FastAPI).
• Familiarity with machine learning and generative AI.
• Deep familiarity with generative AI ecosystems and tooling (e.g., OpenAI, Hugging Face, Lang Chain, Llama Index).
• Experienced with system architecture, design patterns, and API design.
• Experience with infrastructure as code tools.
• Proven experience in a mentorship and/or leadership role.
• Lead the design, development, and deployment of scalable software solutions using Python and AWS.
• Lead architecture design for AI-driven systems, including LLM-based applications, RAG pipelines, and intelligent automation workflows.
• Architect and implement robust, high-performance systems that meet business requirements.
• Define end-to-end solution architectures that integrate AI components with existing enterprise systems.
• Collaborate with cross-functional teams to define technical specifications and ensure alignment with project goals.
• Work directly with clients/stakeholders to understand business challenges and translate them into scalable AI/ML solutions.
• Mentor and guide junior engineers, fostering a culture of continuous learning and improvement.
• Provide technical leadership on AI architecture decisions, trade-offs, and best practices.
• Conduct code reviews and ensure adherence to best practices and coding standards.
• Troubleshoot and resolve complex technical issues, ensuring system reliability and performance.
• Evaluate and optimize AI system performance, including latency, cost, and output quality.
• Stay updated with the latest industry trends and technologies, integrating them into the development process.
• Drive adoption of emerging AI technologies and frameworks where appropriate.
• All other duties as assigned.