Mara

Lead Software Engineer – ML, Agentic Workloads

Mara

full-time

Posted on:

Location Type: Remote

Location: Remote • 🇺🇸 United States

Visit company website
✨ AI Apply
Apply

Job Level

Senior

Tech Stack

CloudGrafanaKubernetesPrometheusPythonPyTorchRay

About the role

  • Lead architecture and development of agentic platforms that integrate multiple models, tools, and knowledge sources into dynamic reasoning systems.
  • Evaluate and deploy foundation and open-source models (LLMs, vision, multimodal) using efficient inference strategies and fine-tuning where applicable.
  • Design and maintain prompt lifecycle pipelines with version control, testing, and CI/CD integration (“PromptOps”).
  • Build and optimize RAG systems—vector database configuration, retriever-generator orchestration, and embedding quality improvement.
  • Implement guardrail frameworks for content safety, hallucination control, and policy enforcement across agentic workflows.
  • Integrate and extend agentic frameworks (LangChain, LangGraph, CrewAI, AutoGen, or equivalent), both in code-based and visual orchestration environments.
  • Collaborate with data, product, and infrastructure teams to design scalable APIs and services that enable model-driven applications.
  • Define observability and evaluation metrics for model performance, latency, and behavior drift in production.
  • Drive best practices for secure AI development, privacy-preserving data handling, and governance of third-party model integrations.
  • Mentor engineers across ML, backend, and platform domains; champion continuous learning and experimentation.

Requirements

  • 8+ years of professional software engineering experience, including 3+ years in ML application development or AI platform engineering.
  • Proficiency in Python, with strong understanding of ML toolchains (PyTorch, Hugging Face, LangChain, MLflow, Ray, etc.).
  • Proven experience with model evaluation, fine-tuning, and deployment across cloud and on-prem environments.
  • Hands-on experience with RAG architectures and vector databases (Weaviate, Milvus, pgvector, LanceDB, FAISS).
  • Deep understanding of prompt design, orchestration, and versioning using CI/CD workflows and automated testing frameworks.
  • Familiarity with agentic systems, both code-driven and visual-builder interfaces (LangGraph Studio, Dust, Flowise, Relevance AI, etc.).
  • Strong knowledge of guardrail techniques (rule-based filters, policy evaluators, toxicity detection, grounding validation).
  • Experience deploying ML systems on Kubernetes and serverless environments with observability (Prometheus, Grafana, OpenTelemetry).
  • Solid understanding of API design, microservice architecture, and data pipeline integration.
  • Excellent communication and leadership skills, with ability to translate complex ML concepts into actionable engineering outcomes.
Benefits
  • Competitive salary
  • Flexible working hours
  • Professional development opportunities

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills
PythonML toolchainsPyTorchHugging FaceLangChainMLflowRayRAG architecturesvector databasesprompt design
Soft skills
communicationleadershipmentoringcollaborationcontinuous learningexperimentation