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Ml Model Fine Tuning Jobs in Virginia (NOW HIRING)

MLOps Architect

Arlington, VA · On-site

$117K - $189K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Data ingestion & preprocessing o Feature engineering / embedding generation o Model training & fine-tuning (traditional ML + foundation models) * Model evaluation & validation * Deployment (real-time ...

Artificial Intelligence Machine Learning Engineer

Ashburn, VA · On-site +1

$117K - $140K/yr

  • Medical

  • Life

  • Retirement

  • PTO

MANTECH seeks a motivated, career and customer-oriented Senior AI ML Engineer . This is currently a ... Model fine-tuning, e.g. LoRA * Embedding-based search and semantic retrieval * Strong understanding ...

AI/ML Subject Matter Expert

Vienna, VA · On-site

$195K - $210K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Experience in fine-tuning LLMs for custom datasets and/or using RAG to augment LLM applications ... Experience developing and assessing AI/ML models and ensembles * Support the seamless integration ...

AI/ML Subject Matter Expert

Vienna, VA · On-site

$100 - $130/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Experience in fine-tuning LLMs for custom datasets and/or using RAG to augment LLM applications ... Experience developing and assessing AI/ML models and ensembles * Support the seamless integration ...

AI-ML Developer

Ashburn, VA · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Proficiency in developing, deploying, and fine-tuning generative AI models, including large ... ML Frameworks: Proficiency in ML Modeling concepts and frameworks like Sklearn, TensorFlow, Keras ...

AI/ML ENGINEER Location: Reston,VA Duration: 12+ Months Visa: USC, GC, H1B and EAD Contract Type ... Experience with fine-tuning or customizing foundation models. * Knowledge of data privacy and ...

AI-ML Developer

Ashburn, VA · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Proficiency in developing, deploying, and fine-tuning generative AI models, including large ... ML Frameworks: Proficiency in ML Modeling concepts and frameworks like Sklearn, TensorFlow, Keras ...

AI-ML Developer

Ashburn, VA · On-site +1

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Proficiency in developing, deploying, and fine-tuning generative AI models, including large ... ML Frameworks: Proficiency in ML Modeling concepts and frameworks like Sklearn, TensorFlow, Keras ...

AI/ML Engineer

Reston, VA · On-site

$175K - $220K/yr

Develop and automate fine-tuning and model training pipelines using available tools or custom code. * Develop innovative AI/ML and LLM-enabled solutions to address specific mission challenges and ...

Responsibilities : • Develop and automate fine-tuning and model training pipelines using available tools or custom code. • Develop innovative AI/ML and LLM-enabled solutions to address specific ...

Design and implement fine-tuning and prompt engineering strategies for LLMs. Build Retrieval Augmented Generation pipelines and scalable NLP services. Develop APIs and deploy ML models to cloud and ...

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Ml Model Fine Tuning information

What is ML model fine-tuning?

ML model fine-tuning is the process of taking a pre-trained machine learning model and making small adjustments to its parameters using new data relevant to your specific task. This approach allows you to leverage the general knowledge the model has already learned, while adapting it to perform better on your particular dataset or problem. Fine-tuning is common in fields like natural language processing and computer vision, as it saves time and resources compared to training a model from scratch. The process typically involves retraining the last few layers of the model or using a lower learning rate for the entire model.

What are some common challenges faced when fine-tuning machine learning models in a production environment?

One common challenge when fine-tuning ML models in production is ensuring that the updated models generalize well to new, unseen data without overfitting to recent trends or noise. Additionally, coordinating with data engineers and software developers is crucial to maintain data pipelines and model deployment workflows. Managing computational resources and keeping track of model versions for reproducibility can also be complex, especially in fast-paced or large-scale environments. Regular communication with stakeholders is important to align model updates with business objectives and to ensure the smooth integration of improvements.

What are the key skills and qualifications needed to thrive as an ML model fine tuning specialist, and why are they important?

To thrive as an ML Model Fine Tuning Specialist, you need a solid background in machine learning, statistics, programming (often Python), and experience with model training and evaluation. Familiarity with frameworks such as TensorFlow, PyTorch, and tools like Hugging Face Transformers, along with experience in managing GPUs and cloud platforms, is typically required. Strong problem-solving skills, attention to detail, and effective communication help you understand project requirements and collaborate with data scientists and engineers. These skills are crucial for optimizing model performance, ensuring accurate results, and delivering robust AI solutions tailored to specific business needs.

What is the difference between Ml Model Fine Tuning vs Data Scientist?

AspectMl Model Fine TuningData Scientist
CredentialsKnowledge of machine learning frameworks, programming skillsDegree in data science, statistics, or related fields
Work EnvironmentFocus on model optimization, coding, and experimentationData analysis, modeling, and interpretation
Industry UsageAI/ML development teams, tech companiesResearch, analytics, business intelligence

While Ml Model Fine Tuning involves adjusting pre-trained models to improve performance, Data Scientists analyze data, develop models, and interpret results. Fine tuning is a specialized task within the broader scope of a Data Scientist's role, often requiring similar technical skills but focusing more on model optimization.

What are popular job titles related to Ml Model Fine Tuning jobs in Virginia?

For Ml Model Fine Tuning jobs in Virginia, the most frequently searched job titles are:

What job categories do people searching Ml Model Fine Tuning jobs in Virginia look for?

The top searched job categories for Ml Model Fine Tuning jobs in Virginia are:

What cities in Virginia are hiring for Ml Model Fine Tuning jobs?

Cities in Virginia with the most Ml Model Fine Tuning job openings:

MLOps Architect

Kapitus

Arlington, VA • On-site

$117K - $189K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 2 days ago


Job description

We are seeking a senior MLOps Architect to design and scale a modern ML and Generative AI platform across AWS. This role will own the architecture for traditional ML and LLM/Generative AI pipelines, ensuring production reliability, governance, cost optimization (FinOps), and enterprise-grade security. The ideal candidate has deep expertise in AWS, SageMaker, Databricks, Atlan (data catalog/governance), and modern MLOps tooling, and understands how to operationalize LLMs, RAG systems, and foundation models within a governed, scalable MLOps stack. This is a strategic, hands-on architecture role responsible for integrating GenAI capabilities into an enterprise ML platform.

What you’ll Do:

MLOps & GenAI Platform Architecture

  • Design and implement scalable ML and LLM infrastructure on AWS (SageMaker, EKS, S3, IAM, Lambda, Step Functions, CloudWatch).
  • Architect end-to-end ML and Generative AI lifecycle workflows:
    • Data ingestion & preprocessing o Feature engineering / embedding generation o Model training & fine-tuning (traditional ML + foundation models)
    • Model evaluation & validation
    • Deployment (real-time, batch, streaming)
    • Monitoring & retraining
  • Integrate LLM pipelines (prompt workflows, RAG architectures, fine-tuning flows) into the enterprise MLOps stack.
  • Define standards for CI/CD/CT pipelines across ML and GenAI workloads.

Generative AI & LLM Operationalization

  • Architect Retrieval-Augmented Generation (RAG) pipelines including:
    • Embedding generation workflows
    • Vector database integration
    • Document ingestion and chunking strategies
    • Retrieval evaluation and monitoring
  • Design and deploy LLM-based services using:
    • Managed services (e.g., SageMaker endpoints, Bedrock-style APIs)
    • Containerized custom inference services
  • Establish prompt versioning, evaluation frameworks, and experiment tracking for LLM systems.
  • Implement guardrails for hallucination control, safety monitoring, bias detection, and usage logging.
  • Define architecture for LLM fine-tuning workflows (including data curation, evaluation, and cost controls).
  • Implement scalable orchestration of LLM pipelines using workflow engines and event-driven patterns.

Deployment, Monitoring & Reliability

  • Architect scalable inference patterns for:
    • Traditional ML models
    • LLM APIs
    • RAG systems
  • Implement model monitoring frameworks for:
    • Performance degradation
    • Drift detection
    • LLM output quality
    • Latency and token usage metrics
  • Define SLAs/SLOs for ML and GenAI systems.
  • Design safe deployment strategies (blue/green, canary, shadow testing).
  • Establish logging, observability, and traceability standards for GenAI systems

 

FinOps & Cost Optimization

  • Implement cost tracking for:
    • Training workloads o GPU utilization
    • Inference endpoints o Token consumption (LLM APIs)
    • Vector database storage
  • Optimize LLM workloads for cost-performance tradeoffs (model size, batching, caching strategies).
  • Design autoscaling and compute optimization strategies for GPU and CPU-based inference.
  • Partner with finance and engineering teams to forecast ML/GenAI infrastructure spend.

Platform Enablement & Standards

  • Define enterprise standards for:
    • Experiment tracking
    • Model registry
    • Prompt registry
    • Artifact management
    • Embedding versioning
  • Provide architectural guidance to data science, AI, and engineering teams.
  • Evaluate and recommend tooling across the ML/GenAI stack (MLflow, feature    stores, vector databases, orchestration tools).
  • Drive documentation and reusable patterns for ML and GenAI development.

What We’re Looking for

 

  • 6+ years of experience in ML engineering, data engineering, or MLOps roles.
  • Proven experience architecting ML platforms in AWS.
  • Strong hands-on experience with SageMaker (training, pipelines, deployment).
  • Experience operationalizing LLM or Generative AI systems in production.
  • Experience building RAG pipelines and integrating vector databases.
  • Experience working with Databricks in production.
  • Experience implementing data governance and catalog systems (e.g., Atlan).
  • Strong understanding of CI/CD principles for ML and GenAI.
  • Experience with containerization (Docker) and orchestration (Kubernetes/EKS).
  • Deep knowledge of infrastructure-as-code (Terraform, CloudFormation).
  • Strong understanding of observability and monitoring for ML systems.
  • Experience implementing cloud cost optimization strategies (FinOps).
  • Strong Python proficiency.
  • Experience with foundation model fine-tuning and parameter-efficient methods.
  • Experience implementing model registries and experiment tracking tools.
  • Experience designing feature stores and embedding stores.
  • Familiarity with AI risk management, bias mitigation, and safety controls.
  • Experience supporting regulated or data-sensitive environments.
  • Platform-level architectural thinking.
  • Deep understanding of how to integrate GenAI into enterprise ML ecosystems.
  • Ability to balance scalability, governance, security, performance, and cost.
  • Strong technical leadership and cross-functional collaboration skills.
  • Hands-on ability to move from architecture design to implementation

Kapitus Total Rewards Package Includes: 

  • Competitive Base Salary Range of $117,800 – $189,000 Kapitus is providing this as a good faith salary range to comply with applicable law. The applicant’s final salary will depend on a number of factors including the applicant’s geographic location, skills, and experience.
  • Annual Incentive Compensation Eligibility  Up to 10% annually
  • Health Insurance: Comprehensive medical, dental, and employer-paid vision plans through UnitedHealthcare (UHC), with various coverage levels available to meet the needs of our employees and their families. Additional perks through UHC include: Sweat Equity, free subscription to the Calm App, UHC rewards, Real Appeal, and Quit For Life.
  • Flexible Spending Account: Set aside pre-tax dollars from your paycheck to pay for qualified out-of-pocket medical, dental, vision, pharmacy or dependent care expenses. 
  • Lifestyle Spending Account: Employer sponsored post-tax benefits that allow reimbursement for expenses related to physical, mental and financial well-being. 
  • 100% Company Paid Insurances: Kapitus fully covers the cost of basic short-term and long-term disability insurance, as well as vision insurance, ensuring our employees have comprehensive protection without any personal expense.
  • Voluntary Insurance: Supplemental life insurance as well as enhanced short- and long-term disability coverage are available through Mutual of Omaha, providing additional security for our employees. Additionally, Colonial Accident and Hospitalization insurances are also available, offering further protection against unforeseen events.
  • Paid Maternity and Parental Leave: Beyond state-mandated leave policies, Kapitus provides company-paid maternity and parental leave, supporting our employees during important family milestones.
  • Commuter Benefits: We offer pre-tax benefits on parking and commuter expenses to cover travel to and from work.
  • LifeBalance Program: Enhance your lifestyle with our LifeBalance membership, which offers discounts on outdoor activities, the arts, health, and fitness. Additional benefits include: 
    • Pet and car insurance discounts.
    • Financial services such as LegalShield.
    • Relaxation and stress management tools.
  • Plum Benefits Discount Program: Access exclusive discounts on shows, travel, car rentals, and more, enriching your personal and family life.
  • Tuition Reimbursement: Pursue further education with up to $5,000 annually in tuition reimbursement, plus opportunities to attend relevant conferences and career development events. Managed through our LSA plan, Kapitus Academy. 
  • Travel Reimbursement: We also offer travel reimbursement for all work-related travel, supporting your involvement in career and personal development activities.
  • Paid Time Off and Sick Time.
  • Retirement Benefits: Our 401K plan is managed through Fidelity. To support your long-term financial goals, the company provides a 25% match on your contributions, up to 6% of your annual salary.

About Kapitus:

Kapitus is one of the most reliable and respected names in small business financing. As both a direct lender and a marketplace built with a trusted network of lending partners, we can provide small businesses with the financing they need when, and how it is needed. We have spent our entire existence building a culture that makes us excited to come to work in the morning. Our company is fast paced, teammates need to be self-directed and have an internal motivation to do the right thing, even when the right thing takes a lot of hard work. We show our teammates our appreciation by offering great benefits, competitive pay and solid opportunity for growth.

Company Mission: At Kapitus, our mission is to help small business owners grow their organizations by providing tailored, transparent, and ethical financing solutions. We invest in every business owner’s story and we are dedicated to building lasting relationships to champion their goals. We promise to keep the best interests of our clients at the center of the financing process by operating with transparency, fairness, and integrity.

Consideration will be given to qualified remote candidates residing in states where Kapitus and/or one of its subsidiaries has an established physical presence.

Company Description

Kapitus is one of the most reliable and respected names in small business financing. As both a direct lender and a marketplace built with a trusted network of lending partners, we can provide small businesses with the financing they need when, and how it is needed.

We have spent our entire existence building a culture that makes us excited to come to work in the morning. Our company is fast paced, teammates need to be self-directed and have an internal motivation to do the right thing, even when the right thing takes a lot of hard work.
We show our teammates our appreciation by offering great benefits, competitive pay and solid opportunity for growth.