Senior AI Software Engineer
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Job Description
Senior AI Software Engineer responsible for advancing enterprise AI initiatives through scalable web applications. Collaborating with teams in software services for complex engineering workflows.
Responsibilities:
- The Senior AI Software Engineer will be responsible for advancing enterprise AI initiatives by extending existing proof‑of‑concept (POC) solutions into scalable, production‑ready web applications that directly support complex engineering workflows.
- This role focuses on transforming early‑stage AI models and prototypes—such as those supporting plan review automation, asset detection, digital delivery, and other domain‑specific use cases—into fully integrated applications accessible through secure, user‑friendly web interfaces across the organization.
- The Senior AI Software Engineer will concentrate on engineering‑related software services, including model ingestion, automated quantity extraction, plan conformance checks, and CI/CD automation.
- The position requires close collaboration with cross‑functional teams (engineering, product, infrastructure, and security) to ensure solutions are robust, compliant, and aligned with enterprise standards and regulatory requirements.
- Produce high‑quality technical documentation, including architecture diagrams, API specifications, deployment runbooks, and user guides.
- Participate in technical planning, backlog grooming, and estimation; contribute to roadmap development for AI/ML capabilities.
Requirements:
- 8+ years of professional software engineering experience, with substantial work in AI/ML and cloud‑native development
- Experience with at least one major cloud platform (AWS, Azure, GCP, or OCI) for deploying and managing ML workloads
- Hands‑on experience with cloud AI/ML services such as Azure AI, AWS SageMaker/Bedrock, GCP Vertex AI, or OCI AI Services
- Strong DevOps background, including:
- Ansible for configuration management and automation
- Docker for containerization
- Kubernetes for container orchestration
- CI/CD best practices for automated build, test, and deployment
- Proficiency with relational and non‑relational databases, including:
- SQL (PostgreSQL, MySQL)
- NoSQL and vector databases for similarity search and embedding‑based retrieval
- Strong scripting skills in both:
- Bash
- PowerShell
- Proven experience designing and maintaining CI/CD pipelines using:
- Azure DevOps
- GitHub Actions
- Jenkins
- Or similar automation tools
- 3–5+ years of production‑level Python development (primary implementation language)
- 3+ years of experience with NLP and LLMs, including:
- Transformer models (BERT, GPT, T5, etc.)
- RAG (Retrieval‑Augmented Generation) systems
- Fine‑tuning and prompt engineering
- Building LLM‑based applications
- 3+ years of experience with time‑series data, including:
- Forecasting models
- Anomaly detection
- Sequential data modeling
- Real‑time monitoring systems
- 3+ years of experience building recommender systems, such as:
- Collaborative filtering
- Ranking models
- Personalization engines
- Content recommendation pipelines
- Production experience with MLOps tools and platforms, such as:
- MLflow
- Weights & Biases
- Kubeflow
- Airflow
- Or similar systems for orchestration, tracking, and model lifecycle management
- Experience with distributed training, including:
- Large‑scale model training
- Multi‑GPU and/or multi‑node setups
- Data/model parallelism and performance optimization
- Production computer vision experience using:
- PyTorch and/or TensorFlow
- OpenCV
- YOLO or similar frameworks for object detection and segmentation
- Real‑time inference and deployment workflows
- Experience with feature stores (e.g., Feast, Tecton) and/or advanced feature engineering techniques
- Hands‑on experience with model optimization techniques:
- Quantization
- Pruning
- Knowledge distillation
- Experience working with LLM ecosystems such as:
- Ollama
- Hugging Face
- Other non‑frontier / open‑weight models
- Demonstrated AI/ML production track record:
- Built and deployed at least 2–3+ ML models serving real users (beyond experimental or research‑only projects)
Benefits:
- Medical, Dental and Vision Insurance
- Wellness Program
- Flexible Spending Accounts (Healthcare, Dependent Care, Commuter)
- Short-Term and Long-Term Disability options
- Basic Life and AD&D Insurance (Company Provided)
- Voluntary Life and AD&D options
- 401(k) Retirement Savings Plan with matching after one year
- Paid Time Off
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