Senior Software Engineer - AI/ML

Job Description

Senior Software Engineer - AI/ML
Salary Range: $140,000-$180,000
About the Role
We are seeking a Senior Software Engineer specializing in AI and Machine Learning to join an innovative AI engineering organization. This role sits at the intersection of applied machine learning, LLM product development, and production-grade software engineering.


The Senior Software Engineer will be responsible for building, training, evaluating, and deploying AI/ML models and agentic systems that power intelligent products and enterprise applications. The ideal candidate combines deep hands-on expertise in PyTorch, transformer architectures, LLMs, and the full machine learning lifecycle with the software engineering discipline required to build reliable, scalable AI solutions in production.


This is an opportunity to play a key role in shaping AI capabilities within a large-scale enterprise environment, working closely with engineering, product, data, architecture, and leadership teams.
Key ResponsibilitiesAI / ML Engineering

• Design, develop, train, fine-tune, and evaluate machine learning models using PyTorch and related libraries.
  
  
• Build and maintain ML training pipelines, experiment tracking workflows, and model evaluation frameworks.
  
  
• Implement transformer-based models and large language model (LLM) integrations for production use cases, including NLP, information extraction, classification, and generation.
  
  
• Apply parameter-efficient fine-tuning techniques such as LoRA, QLoRA, and PEFT to adapt foundation models to domain-specific applications.
  
  
• Design and implement Retrieval-Augmented Generation (RAG) architectures using vector databases and semantic search pipelines.
  
  
• Optimize model inference for latency and throughput through quantization, batching, caching, and other performance techniques.
  
  
• Develop and maintain AI evaluation frameworks, including automated evaluations and tests, to ensure model behavior is reliable, safe, and production-ready.
  
  

LLM Integration & Agentic Systems

• Design and implement LLM-powered agentic workflows using frameworks such as LangChain and LangGraph.
  
  
• Build multi-step reasoning pipelines, tool-calling agents, and autonomous task execution systems that integrate with enterprise data and product APIs.
  
  
• Develop prompt engineering strategies, few-shot templates, structured outputs, and validation mechanisms.
  
  
• Implement runtime guardrails, failure taxonomies, and quality standards for AI-powered applications.
  
  
• Contribute to enterprise AI infrastructure and context-management capabilities that allow AI agents to securely interact with organizational tools, systems, and data.
  
  

MLOps & Production Engineering

• Build and maintain MLOps infrastructure supporting model training, experiment tracking, versioning, and deployment.
  
  
• Containerize and deploy ML services using Docker and Kubernetes and integrate them with CI/CD pipelines.
  
  
• Monitor model performance in production and implement drift detection, feedback loops, and automated retraining processes.
  
  
• Ensure AI systems meet organizational security, privacy, and compliance requirements, including appropriate data minimization and access controls.
  
  
• Partner with data engineering teams to design and maintain feature stores, data pipelines, and training data infrastructure.
  
  

Collaboration & Technical Leadership

• Partner with architecture and AI leadership to define AI architecture patterns, engineering standards, and best practices.
  
  
• Collaborate with product managers, UX designers, and software engineers to translate AI capabilities into well-designed product features.
  
  
• Conduct code reviews with an emphasis on reproducibility, correctness, maintainability, and production readiness.
  
  
• Mentor engineers on AI engineering fundamentals, LLM integration patterns, and responsible AI practices.
  
  
• Stay current with the rapidly evolving AI/ML landscape and evaluate emerging models, frameworks, and techniques.
  
  
• Contribute to organizational AI maturity initiatives and help establish scalable AI engineering practices.
  
  
• Represent AI engineering practices in architecture and technical review forums.
  
  

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, Mathematics, or a related quantitative field.
  
  
• 6-10+ years of professional software engineering experience , including at least 3+ years focused on production ML/AI engineering .
  
  
• Expert-level proficiency in Python and strong familiarity with the Python ML/AI ecosystem.
  
  
• Hands-on production experience with PyTorch , including model development, custom training loops, autograd, GPU acceleration/CUDA, and model serialization.
  
  
• Experience with Hugging Face Transformers, Datasets, and PEFT or comparable libraries.
  
  
• Demonstrated experience building and evaluating RAG pipelines , including chunking strategies, embeddings, vector stores, and retrieval evaluation.
  
  
• Production experience integrating LLM APIs and/or open-source LLMs and building reliable prompt engineering systems.
  
  
• Experience with LangChain, LangGraph , or comparable frameworks for agentic and tool-calling workflows.
  
  
• Strong understanding of machine learning fundamentals, including supervised and unsupervised learning, loss functions, regularization, evaluation metrics, and statistical validation.
  
  
• Experience with experiment tracking and reproducible ML workflows using tools such as MLflow, Weights & Biases, or Comet .
  
  
• Working knowledge of Docker and cloud-based ML platforms such as AWS SageMaker, Azure ML, or equivalent.
  
  
• Experience with SQL and NoSQL databases and designing data pipelines for ML training and inference.
  
  

Preferred Qualifications

• Experience with additional deep learning frameworks such as TensorFlow or JAX , or experience with framework interoperability such as ONNX.
  
  
• Familiarity with computer vision or speech/audio processing.
  
  
• Experience with model compression techniques including quantization, pruning, and knowledge distillation.
  
  
• Experience serving ML models at scale using Triton Inference Server, TorchServe, Ray Serve, or similar technologies.
  
  
• Contributions to open-source ML projects or published research, technical papers, patents, or technical blog posts.
  
  
• Experience with responsible AI frameworks, bias evaluation, model governance, and AI risk management.
  
  
• Familiarity with Model Context Protocol (MCP) and development of MCP servers or comparable tool-integration architectures.
  
  
• Experience working in payroll, fintech, media, enterprise SaaS, or other complex enterprise environments.
  
  
• Experience with Kubernetes-based ML workload orchestration, such as Kubeflow or similar platforms.
  
  

Work Environment & Additional Requirements

• Hybrid work environment with flexibility for remote work.
  
  
• Ability to participate in an on-call rotation as needed for production AI incidents and model deployment events.
  
  
• Access to cloud-based, GPU-accelerated computing environments for model training and experimentation.
  
  
• Ability to work for extended periods at a computer workstation.
  
  
• Ability to use standard computer equipment, including keyboard and mouse.
  
  
• Occasional availability for early-morning or evening meetings to collaborate with distributed teams or international partners.
  
  

What You'll Bring
The successful candidate will bring a combination of strong software engineering fundamentals, deep AI/ML expertise, and a production mindset . You should be comfortable moving from experimentation and model development through deployment, monitoring, and continuous improvement.


You will have the opportunity to work on challenging AI problems, influence technical direction, mentor other engineers, and help build intelligent systems that deliver measurable value within an enterprise environment.

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