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Manager Machine Learning Visa Sponsorship Jobs in Indiana

This position is not eligible for current or future visa sponsorship. The Gen AI Engineer is ... Collaborate closely with the MLOps, product teams, business stakeholders, machine learning ...

This position is not eligible for current or future visa sponsorship. The Gen AI Engineer is ... Collaborate closely with the MLOps, product teams, business stakeholders, machine learning ...

Machinist II - 2nd shift

Indianapolis, IN ยท On-site

$53K - $70K/yr

Sponsorship is not available for applicants for US Work visa status for this opportunity (no ... Insure daily machine and crane PMs are being completed. * Understand SAP confirmations and insure ...

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Manager Machine Learning Visa Sponsorship information

What is a manager machine learning visa sponsorship?

A Manager Machine Learning Visa Sponsorship is a managerial position in the field of machine learning where the employer is willing to sponsor a work visa for qualified international candidates. This role involves leading a team of machine learning engineers or data scientists, overseeing projects, and ensuring the successful development and deployment of machine learning models. The visa sponsorship component means the company will support the candidate through the legal process required for them to work in a different country, typically the United States or other countries with strict work visa requirements. Often, these roles require significant technical expertise and leadership experience, as well as familiarity with immigration processes.

What are the key skills and qualifications needed to thrive as a manager machine learning, and why are they important?

To thrive as a Manager, Machine Learning, you need a strong background in computer science, statistics, and machine learning, typically supported by an advanced degree and experience leading technical teams. Familiarity with tools like Python, TensorFlow, PyTorch, and cloud platforms, as well as knowledge of data management and ML lifecycle systems, is essential. Strong leadership, communication, and problem-solving skills set standout candidates apart, enabling effective team management and stakeholder collaboration. These capabilities are crucial for driving successful ML projects, fostering innovation, and ensuring alignment with organizational objectives.

What are the main challenges faced by a manager machine learning, especially when leading a team with diverse technical backgrounds?

As a Manager, Machine Learning, one of the main challenges is bridging the gap between team members with varied expertise, such as data scientists, engineers, and product managers. Ensuring clear communication, aligning on project goals, and fostering collaboration are essential. Additionally, balancing hands-on technical guidance with strategic leadership, while keeping up with rapidly evolving machine learning technologies, requires adaptability and strong organizational skills. Successfully navigating these challenges can lead to innovative solutions and a highly effective team.

What are the most commonly searched types of Machine Learning Visa Sponsorship jobs in Indiana?

The most popular types of Machine Learning Visa Sponsorship jobs in Indiana are:

What cities in Indiana are hiring for Manager Machine Learning Visa Sponsorship jobs?

Cities in Indiana with the most Manager Machine Learning Visa Sponsorship job openings:

Senior Software Engineer Applied AI

Advanced Monitored Caregiving Inc.

Indianapolis, IN โ€ข Remote

$117K - $154K/yr

Full-time

Re-posted 15 hours ago


Job description

Senior Software Engineer: Applied AI (Voice Agents & ML Systems)

AMC Health · Remote (US) · Full-time

The pitch

We build and operate production AI voice agents that hold real phone conversations in a regulated healthcare setting, plus the machine learning and LLM pipelines around them. This is one seat that spans four disciplines that rarely come together: real-time systems, LLM engineering, traditional machine learning, and serious cloud infrastructure, all in production, all with real consequences. If you are the kind of engineer who gets restless doing one thing, this role is the opposite problem.

What you'll work across

Real-time voice AI

  • Streaming, low-latency speech-to-speech systems built on modern LLMs
  • Telephony and real-time media (call control, live audio streaming)
  • Audio handling and the quirks of real human conversation (interruptions, timing, noise)
  • Concurrency on a latency-sensitive path, where p99 matters and a stall is something a caller hears

LLM engineering

  • Wrapping nondeterministic models in deterministic control so they behave reliably in production
  • Multi-model pipelines, prompt design, and cost/latency budgeting
  • Evaluation harnesses, including LLM-as-judge and automated agent-tests-agent approaches
  • Agentic tooling that gives AI systems safe, structured access to infrastructure

Traditional (non-LLM) machine learning

  • End-to-end ML pipelines: feature engineering, model training, and scheduled inference
  • Imbalanced, messy real-world data; calibration and explainability for non-technical consumers
  • Turning research notebooks into reproducible, auditable production pipelines

Cloud and infrastructure

  • Infrastructure as code across multiple environments (we run on AWS)
  • Managed compute, data, streaming, and orchestration services
  • Security engineering in a regulated setting: encryption, least-privilege access, strict data-handling discipline
  • Observability and telemetry-driven debugging, tracing a production issue from a metric anomaly to root cause

Plus occasional full-stack work on internal tools, and an engineering workflow that leans heavily on AI coding assistants, with human accountability for every change.

What you'll actually do

  • Ship and debug code on a live, real-time voice pipeline where latency and correctness are user-facing
  • Design control systems around LLMs: guardrails, budgets, watchdogs, safe fallbacks
  • Build and operate LLM evaluation and batch-analysis pipelines
  • Own traditional ML workflows from data to scheduled production inference
  • Trace production issues from a metric anomaly to root cause, including building the evidence when the cause is a vendor

Must-haves

  • 7+ years building and operating production backend systems, with strong general-purpose programming skills (we work primarily in Python)
  • Experience running distributed systems in the cloud; comfortable debugging from telemetry to root cause
  • Hands-on production experience with LLMs or generative AI (any provider or framework), plus the judgment to know when not to use a model
  • Working fluency across the traditional machine learning lifecycle (you productionize; you do not need to publish)
  • Disciplined in a regulated environment: small, reviewable changes and careful handling of sensitive data

Nice-to-haves

  • Real-time media or telephony experience
  • Front-end / full-stack ability
  • ML pipeline experience, vector search, or embeddings
  • Fluency with AI coding assistants (our workflows assume them, with human accountability for every change)

How we work

Smallest correct change wins. Every behavior change is validated against the live system. Evidence over opinion in debugging. Code review is rigorous. Safety and privacy gate everything.

Work authorization (no exceptions)

This role is open only to US citizens and lawful permanent residents (Green Card holders). We cannot consider candidates who require visa sponsorship now or in the future, and we are unable to make exceptions of any kind.

How to apply

Please submit both of the following:

  • Your LinkedIn profile URL
  • A phone number where we can reach you

A resume is welcome but optional; the two items above are required.