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Python Ml Developer Jobs in Huntington, NY (NOW HIRING)

Soni Resources

Senior AI/ML Engineer

New York, NY · On-site

$200K/yr

We are seeking a Senior AI/ML Engineer to help design, build, and scale advanced analytics and ... Strong proficiency in Python and SQL * Experience working with both structured and unstructured ...

Capgemini

AI Solutions Developer

New York, NY · On-site

$55 - $75.75/hr

We are seeking a highly skilled AI Solutions Developer with strong expertise in building end to end AI/ML applications using Python and modern AI frameworks such as LangChain PyTorch and TensorFlow.

Paramount

ML Platform Engineer

Manhattan, NY

$130K - $195K/yr

ML Platform Engineer Department : Data & Insight Group Location: New York City, Los Angeles, San ... Deep experience developing as a team in Python Deep experience designing and implementing MLOps ...

Tennr

Senior ML Infrastructure Engineer

New York, NY · On-site

$200K - $230K/yr

Strong software engineering fundamentals, with proficiency in Python and TypeScript * Experience in software design and architecture for highly available ML systems for use cases like inference ...

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All JobsPython Ml Developer JobsPython Ml Developer Jobs in Huntington, NY

Python Ml Developer information

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How much do python ml developer jobs pay per hour?

As of Jul 15, 2026, the average hourly pay for python ml developer in Huntington, NY is $62.03, according to ZipRecruiter salary data. Most workers in this role earn between $51.11 and $70.48 per hour, depending on experience, location, and employer.

What does a Python ML Developer do?

A Python ML Developer designs, builds, and deploys machine learning models using the Python programming language. They work with large datasets, clean and process data, select appropriate algorithms, and use libraries like TensorFlow, PyTorch, or scikit-learn to implement solutions. Their work often involves collaborating with data scientists and engineers to integrate machine learning models into applications. Additionally, they may be responsible for testing, tuning, and optimizing models to achieve the best possible performance in real-world scenarios.

What are some common challenges Python ML Developers face when deploying machine learning models to production?

Python ML Developers often encounter challenges such as ensuring model scalability, managing dependencies, and maintaining reproducibility when deploying models into production environments. Integrating machine learning models with existing systems can require close collaboration with DevOps and software engineering teams to streamline workflows and automate deployment pipelines. Additionally, monitoring model performance over time and handling data drift are crucial responsibilities to ensure continued accuracy and reliability of deployed solutions.

Will MLE be replaced by AI?

Machine Learning Engineers (MLEs) design, develop, and maintain AI and machine learning systems. While AI automation tools can handle certain tasks, MLEs are essential for creating, optimizing, and interpreting complex models, making complete replacement unlikely in the near term. MLEs need skills in programming, data analysis, and model deployment to adapt to evolving AI technologies.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-paying position in artificial intelligence, such as senior machine learning engineer or AI research director, often requiring advanced skills in deep learning, data science, and programming with tools like Python and TensorFlow. Such roles usually involve leadership, strategic planning, and extensive experience in the field.

Which 3 jobs will survive AI?

For a Python ML Developer, roles that require complex problem-solving, creativity, and human judgment are likely to persist, such as AI research scientist, data scientist, and software engineer. These jobs involve designing, interpreting, and improving AI models, which currently require advanced expertise, critical thinking, and domain knowledge that AI cannot fully replicate. Continuous learning and staying updated with new tools and techniques are essential for long-term career resilience.

What are the key skills and qualifications needed to thrive as a Python ML Developer, and why are they important?

To thrive as a Python ML Developer, you need strong programming skills in Python, a solid understanding of machine learning algorithms, and a background in mathematics or statistics, often supported by a degree in computer science, engineering, or a related field. Familiarity with tools and libraries such as TensorFlow, scikit-learn, PyTorch, and version control systems like Git is essential, along with experience using data visualization and cloud platforms. Critical soft skills include problem-solving, adaptability, and effective communication to collaborate with cross-functional teams and explain complex models to stakeholders. These skills ensure the successful development, deployment, and maintenance of machine learning solutions that drive business value.

What is the difference between Python Ml Developer vs Data Scientist?

AspectPython Ml DeveloperData Scientist
Required CredentialsBachelor's in CS, Data Science, or related; Python, ML certificationsBachelor's/Master's in Data Science, Statistics, or related; Python, ML certifications
Work EnvironmentSoftware development teams, AI/ML projectsResearch, data analysis, modeling teams
Employer & Industry UsageTech companies, startups, AI firmsFinance, healthcare, tech, research institutions
Common Search & ComparisonYesYes

Python ML Developers focus on building and deploying machine learning models using Python, often working closely with software engineering teams. Data Scientists analyze data, create models, and generate insights, often using Python along with statistical tools. While both roles require Python and ML knowledge, Python ML Developers are more involved in implementation and deployment, whereas Data Scientists focus on data analysis and research.

Can you do ML in Python?

Yes, Python is widely used for machine learning (ML) development due to its extensive libraries such as TensorFlow, scikit-learn, and PyTorch. Python skills are essential for a Python ML developer to build, train, and deploy ML models efficiently in various environments.
What job categories do people searching Python Ml Developer jobs in Huntington, NY look for? The top searched job categories for Python Ml Developer jobs in Huntington, NY are:
What cities near Huntington, NY are hiring for Python Ml Developer jobs? Cities near Huntington, NY with the most Python Ml Developer job openings:
Python Ml Developer jobs near you
Gen AI / ML Full Stack Engineer, orchestration, Python/Java, RAG, LLMOps, Vectors, 12+ Mths Cont NYC

Gen AI / ML Full Stack Engineer, orchestration, Python/Java, RAG, LLMOps, Vectors, 12+ Mths Cont NYC

Zen & Art

Manhattan, NY • On-site

Other

Posted 4 days ago

Job description

Gen AI / ML Full Stack Engineer, orchestration, Python/Java, RAG, LLMOps, Vectors, 12+ Mths Cont NYC

JPC 3566

Level 3 : 5 to 8 Years of Industry exp

LOCATION: New York ( 3 days hybrid, inperson interview will be needed )

Duration : 12+ months

Title: Gen AI / ML Full Stack Engineer , orchestration framework (Langchains etc), Python/Java, RAG, LLMOps, Adv Vectors (ColBERT/chunking), production failures, Fixed Income / Lending Platforms 12+ Mths Cont NYC

Description:

Applied AI Engineer Overview

Morgan Stanleys Fixed Income Institutional Lending Technology team is building an enterprise grade GenAI workflow platform to enable document data extraction, embedded productivity assistants, and automated business workflows across business lines.

This is not a research or demo role.

We are seeking senior hands-on full stack engineers who have designed, built, and operated GenAI systems in production, and understand failure modes, evaluation, and governance as first class AI-powered systems.

What Youll Do Design and evolve reusable GenAI workflows used across Lending business lines.

  • Develop an enterprise grade AI-based document ingestion and data extraction capability, including traceability, confidence scoring, and human-in-the-loop review.
  • Build AI-powered assistants embedded in Lending systems using agentic workflows.
  • Deliver automated content and deck generation workflows for reporting and approvals.
  • Provide expert advice on GenAI architecture including model selection, orchestration patterns, and evaluation strategy.
  • Establish LLMOps practices: extraction accuracy, assistant reliability, prompts management, and audit monitoring.
  • Design and implement controls for entitlements, PII handling within open-source models in a regulated environment.
  • In the role you are expected to act as a hands-on technical expert, and it has a clear path to becoming a platform owner responsible for shared GenAI standards across Lending.

What Youll Bring

  • 2+, dedicated experience in practical application of GenAI solutions in an enterprise business environment.
  • Designing and operating GenAI orchestration frameworks in production beyond vendor examples (e.g., LangChain systems),
  • 5+ years of strong front-to-back engineering experience, focusing on AI ML platforms and workflows (Python or Java).
  • Proven experience building and operating production grade GenAI / LLM platforms, applying patterns such as RAG, tool/function calling, agentic workflows, and validated structured outputs.
  • Strong LLMOps expertise, including evaluation harnesses, prompt and version management, regression testing, observability, and reliability measurement in production systems.
  • Hands on experience building AI-first data ingestion pipelines with measurable quality, accuracy, and reliability.
  • Advanced retrieval experience advanced vector search, including multi vector and late interaction approaches (e.g., ColBERT, chunking), multi stage retrieval pipelines, metadata filtering, reranking.
  • Solid understanding of evaluation metrics and how they shape practical RAG system design (e.g., recall vs precision, latency vs quality, MRR, NDCG).
  • Experience operating GenAI systems through real production failures (model regressions, retrieval degradation, prompt drift, data quality issues) and designing mitigation strategies.
  • Nice to Have Fixed Income or Institutional Lending domain experience. Experience working in regulated environments with strong audit and control requirements.
  • Familiarity with enterprise security, data governance, and entitlement models.
  • Experience designing reusable internal platforms or shared developer tooling. Frontend experience is beneficial (Angular or React)

Apply