Tenacity Python Jobs in Usa

Role: Senior Engineer Python-AI_ML-GenAl

Location: Phoenix-AZ

Term: Contract

• Proficiency with distributed/multi-tier systems and application development, including excellent programming skills in languages such as Python.
• Hands-on experience designing and deploying Generative Al Applications using LLM frameworks (e.g. LangChain Llamalndex, Semantic Kernel), embeddings, and vector databases.
• Experience building Agentic Al systems with autonomous planning, tool/function calling, memory management, RAG, and multi-agent orchestration.
• Solid understanding of ML/DL fundamentals, prompt engineering, model evaluation, and responsible Al practices.
• Experience integrating Al solutions with enterprise systems, APls, event streaming (e.g., Kafka), and relational/NoSQL databases.
• Familiarity with cloud platforms (AWS/Azure/GP), containers (Docker), and CI/CD pipelines (Git,
• Jenkins, Maven).
• Strong foundation in data structures, system design, APIs (REST/HTTP), and Unix/Linux environments.
• Excellent communication skills with the ability to translate complex Al concepts into scalable business solutions.
• Good understanding of data structures, algorithms and design patterns
• Excellent communication skills, with the ability to influence at all levels across functions, from both technical and non-technical perspectives alike.

Role: Java-Python-Senior Eng

Location: Phoenix-AZ

Term: Contract

• 8+ years of commercial software development experience
• Proficiency with distributed/multi-tier systems and application development, including excellent programming skills in languages such as Java, Python
• Hands-on experience with Spring Boot, Micro-services, Kafka, Relational and/or NoSQL databases.
• Experience in conversational interfaces, chatbots, machine learning, deep learning, and data analytics.
• Experience with Agile development, Continuous Integration, and Continuous Delivery, including working knowledge of various tools in the CI/CD pipeline.
• Experience with automated release management using Maven, Salt, Git, Jenkins
• Good understanding of web technologies (HTTP, Apache) and familiarity with Unix/Linux
• Good understanding of data structures, algorithms, and design patterns
• Great written communication and documentation abilities
• Looks proactively beyond the obvious for continuous improvement opportunities.
• Knowledge & experience of Contact center ecosystem/Genesys Framework and Architecture, familiarity with the Genesys ecosystem would be preferrable.
• Leadership and communication: lead teams and collaborate with stakeholders, so strong leadership and communication skills are essential.
• Excellent communication skills, with the ability to influence at all levels across functions, from both technical and non-technical perspectives alike.

Machine Learning Engineer | Python | Pytorch | Distributed Training | Optimisation | GPU | Hybrid, San Jose, CA

Title: Machine Learning Engineer

Location: San Jose, CA

Responsibilities:

• Productize and optimize models from Research into reliable, performant, and cost-efficient services with clear SLOs (latency, availability, cost).
• Scale training across nodes/GPUs (DDP/FSDP/ZeRO, pipeline/tensor parallelism) and own throughput/time-to-train using profiling and optimization.
• Implement model-efficiency techniques (quantization, distillation, pruning, KV-cache, Flash Attention) for training and inference without materially degrading quality.
• Build and maintain model-serving systems (vLLM/Triton/TGI/ONNX/TensorRT/AITemplate) with batching, streaming, caching, and memory management.
• Integrate with vector/feature stores and data pipelines (FAISS/Milvus/Pinecone/pgvector; Parquet/Delta) as needed for production.
• Define and track performance and cost KPIs; run continuous improvement loops and capacity planning.
• Partner with ML Ops on CI/CD, telemetry/observability, model registries; partner with Scientists on reproducible handoffs and evaluations.

Educational Qualifications:

• Bachelors in computer science, Electrical/Computer Engineering, or a related field required; Master’s preferred (or equivalent industry experience).
• Strong systems/ML engineering with exposure to distributed training and inference optimization.

Industry Experience:

• 3–5 years in ML/AI engineering roles owning training and/or serving in production at scale.
• Demonstrated success delivering high-throughput, low-latency ML services with reliability and cost improvements.
• Experience collaborating across Research, Platform/Infra, Data, and Product functions.

Technical Skills:

• Familiarity with deep learning frameworks: PyTorch (primary), TensorFlow.
• Exposure to large model training techniques (DDP, FSDP, ZeRO, pipeline/tensor parallelism); distributed training experience a plus
• Optimization: experience profiling and optimizing code execution and model inference: (PTQ/QAT/AWQ/GPTQ), pruning, distillation, KV-cache optimization, Flash Attention
• Scalable serving: autoscaling, load balancing, streaming, batching, caching; collaboration with platform engineers.
• Data & storage: SQL/NoSQL, vector stores (FAISS/Milvus/Pinecone/pgvector), Parquet/Delta, object stores.
• Write performant, maintainable code
• Understanding of the full ML lifecycle: data collection, model training, deployment, inference, optimization, and evaluation.

Machine Learning Engineer | Python | Pytorch | Distributed Training | Optimisation | GPU | Hybrid, San Jose, CA

Onsite AI Engineer - Construction Industry Focus

New Haven, CT - Onsite 5 days per week

• Initial Assignment: Fully onsite 5 days per week at a construction site in Ft. Myers (FL) or New Haven (CT) for 1 year
• Post-Assignment: Relocation to one of the corporate offices for hybrid employment: Boston, MA (preferred), New York City (NY), New Haven (CT), Herndon (VA), West Palm Beach (FL), or Estero (FL)

Role Summary

As the on-site catalyst who turns AI ideas into working reality. Partnering with each project’s AI Champion (Project Manager or Superintendent), you’ll uncover pain points, redesign workflows, and deploy AI agents that cut down reporting, accelerate RFIs, simplify lookahead planning, progress updates, materials tracking, and more. When needed, you will develop user stories and coordinate development with the central AI Studio. You’ll help advance the vision of the “Construction Site of the Future,” showing how agentic AI will transform project operations.

Responsibilities

• Workflow discovery and redesign: Lead Lean/Six Sigma workshops; map value streams; log high-impact AI agent opportunities that improve field efficiency.
• AI agent development: Build and deploy multiple production-ready AI agents using Copilot Studio, Power Apps/Automate, ChatGPT Enterprise, or code-first frameworks. Integrate agents into Teams/SharePoint on the front end and Databricks Lakehouse or other enterprise data sources on the back end.
• RAG pipelines and LLMOps: Design and operate retrieval-augmented generation (RAG) pipelines with Databricks Delta Tables, Unity Catalog, and Vector Search (or Spark/Hadoop equivalents). Monitor cost, latency, adoption, and model drift.
• Cross-cloud orchestration: Blend OpenAI, Azure OpenAI, and AWS Bedrock services through secure custom connectors to maximize flexibility and adoption.
• Data integration: Partner with Data Engineering to deliver ETL/ELT pipelines, API integrations, and event-driven connectors that feed RAG pipelines and AI agents.
• Change management and adoption: Train field teams, gather feedback, iterate quickly, and embed agents into SOPs. Track usage and ROI with adoption metrics and behavior-change KPIs.
• Stakeholder communication: Translate technical results into business value for leadership and clients. Contribute use cases and playbooks for the “Construction Site of the Future.”
• Compliance and hand-offs: Ensure all AI solutions meet the company’s data governance and security standards. Draft clear user stories and specs for escalation to central AI/Data Engineering teams when necessary.

Qualifications

• 4+ years in AI engineering, data science, or ML-focused software engineering.
• Proven experience building multiple AI agents in production environments.
• 2+ years of hands-on experience with LLMs, RAG pipelines, and LLMOps practices.
• Must have strong traditional software engineering background in Python

Bonus Points

• Experience in construction, manufacturing, or other process-heavy industries.
• Advanced degree in a technical field.