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Tensorflow Jobs in Connecticut (NOW HIRING)

The Hartford

Sr Applied AI Data Scientist

Hartford, CT

$110.72K - $166.08K/yr

Strong foundation in ML, deep learning, NLP; familiarity with PyTorch/TensorFlow and generative AI. * Experience with cloud tools (Google Vertex AI, AWS SageMaker/Bedrock). * Ability to build ...

Prophecy Technologies

AI / GenAI Engineer

Hartford, CT · On-site

$104.90K - $141.20K/yr

... tools: - TensorFlow - PyTorch - Transformers - LangChain - LangGraph - Autogen - LlamaIndex • Experience with NLP techniques (text generation, summarization, understanding) • Experience with ...

Commonfund

AI/ML Development Analyst

Norwalk, CT · On-site

$100K - $150K/yr

Experience with ML frameworks as TensorFlow, PyTorch, or Scikit-learn * Strong analytical, problem-solving, and debugging skills. * Ability to work independently and collaboratively in a fast-paced ...

Hyperfine

Senior AI Scientist

Guilford, CT · On-site

$156K - $179K/yr

Programming experience in PyTorch and/or TensorFlow. Physical Demands: * This is a full-time, on-site role based out of Hyperfine's office in Guilford, CT. * Available nights, weekends, and holidays ...

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$35.7K

$116.8K

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How much do tensorflow jobs pay per year?

As of May 29, 2026, the average yearly pay for tensorflow in Connecticut is $116,759.00, according to ZipRecruiter salary data. Most workers in this role earn between $93,700.00 and $129,400.00 per year, depending on experience, location, and employer.

What is a TensorFlow job?

A TensorFlow job typically involves developing, training, and deploying machine learning models using TensorFlow, an open-source AI framework. Responsibilities may include data preprocessing, building neural networks, optimizing model performance, and integrating models into applications. These roles are common in industries like healthcare, finance, and autonomous systems, requiring skills in Python, deep learning, and TensorFlow's ecosystem.

What are the key skills and qualifications needed to thrive in the Tensorflow position, and why are they important?

To thrive in a TensorFlow Developer role, you need strong programming skills in Python, deep learning knowledge, and hands-on experience with TensorFlow and related AI frameworks. Familiarity with tools like Keras, TensorBoard, and cloud platforms such as Google Cloud is often required, and TensorFlow Developer certifications are highly valued. Excellent problem-solving, communication, and teamwork skills help professionals navigate complex projects and collaborate effectively with cross-functional teams. These skills and qualities ensure the successful design, deployment, and optimization of machine learning models in real-world applications.

What are typical daily responsibilities for someone working in a TensorFlow Developer role?

As a TensorFlow Developer, your day-to-day responsibilities often include designing and building machine learning models, preprocessing data, conducting model training and evaluation, and deploying models to production environments. You may also work closely with data scientists, software engineers, and product managers to identify use cases, define project requirements, and optimize system performance. Regular tasks can involve using tools for data visualization, debugging, and performance tuning, as well as keeping up with the latest advancements in machine learning techniques. Collaboration and clear communication are key, as projects often require input and feedback from multiple technical and non-technical stakeholders.
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Tensorflow jobs near you
Infographic showing various Tensorflow job openings in Connecticut as of May 2026, with employment types broken down into 1% Internship, 72% Full Time, 23% Part Time, and 4% Contract. Highlights an 81% Physical, 3% Hybrid, and 16% Remote job distribution, with an average salary of $116,759 per year, or $56.1 per hour.

Machine Learning Engineer (MLOps / Production ML Engineer)

Info Dinamica Inc

Hartford, CT • On-site

Other

This job post has expired today. Applications are no longer accepted.

Job description

Translate data science prototypes into production-grade ML services and pipelines.- Build training and inference code with reproducibility, versioning, and automated testing.- Implement scalable model serving (online/offline), batching, and latency/throughput optimization.- Integrate model lifecycle tooling (tracking, registry, deployment automation, monitoring).- Collaborate with Data Engineering on feature pipelines and data contracts.- Own production health drift detection, performance regression, rollback strategies, and incident response.""- 5+ years software engineering with 2+ years shipping ML models to production.- Strong Python skills and experience with ML frameworks (TensorFlow/PyTorch).- Experience with containers and orchestration (Docker/Kubernetes) and API development.- Understanding of ML system design (data leakage, training-serving skew, drift).- CI/CD and DevOps practices applied to ML workloads (MLOps).""- Experience with feature stores, model registries, and model monitoring stacks.- GPU optimization and distributed training experience.- Experience with responsible AI toolkits and compliance requirements."Python, TensorFlow, PyTorch, Docker, REST APIs

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