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About This Role
Malvern, Pennsylvania 19355 Posted October 20th, 2026
Job Type: Full Time
Job Category: IT
Job Description
Job Title: Forward Deployed Engineer (Python/PySpark + Claude + SQL + APIs + Data & MLOps) Location: NJ/NYC/ Charlotte/Columbus OH/ Pittsburg/Boston/ Malvern, PA/Washington DC Full Time
Job Description Location: NJ/NYC/ Charlotte/Columbus OH/ Pittsburg/Boston/ Malvern, PA/Washington DCMust Have Technical/Functional Skills
- 10+ years of experience in Data Engineering, AI Application Development, Cloud Data Platforms, and Production-grade Software Engineering.
- Strong hands-on expertise in Python, PySpark, SQL, REST APIs, FastAPI, and enterprise application integration.
- Experience designing and building AI-powered applications using Claude, Prompt Engineering, Retrieval-Augmented Generation (RAG), Vector Databases, Semantic Search, and Agentic AI patterns.
- Deep understanding of Data Engineering frameworks, including ETL/ELT, batch and streaming pipelines, data quality, metadata management, lineage, observability, and DataOps practices.
- Experience implementing and operationalizing MLOps / LLMOps capabilities including model lifecycle management, CI/CD pipelines, MLflow, monitoring, deployment automation, rollback, and production support.
- Strong knowledge of cloud and modern data platforms, including compute, storage, integration, orchestration, security, networking, monitoring, and cost optimization services.
- Hands-on experience with Git, Docker, Kubernetes, Airflow, MLflow, CI/CD, and distributed data processing frameworks.
- Strong understanding of enterprise security and governance principles, including IAM/RBAC, encryption, secrets management, privacy controls, auditability, compliance, and secure data access.
- Ability to translate business requirements into scalable solution designs, define non-functional requirements, estimate effort, and drive production-ready implementation.
- Experience working with Banking, Financial Services, or Insurance (BFSI) data environments and familiarity with regulatory, security, privacy, and resilience requirements.
- Strong client-facing consulting skills with experience conducting discovery workshops, solution shaping, rapid prototyping, stakeholder communication, and technical presentations.
- Bachelor’s degree in computer science, Engineering, Information Systems, Data Management, or a related discipline.
- Preferred certifications in Cloud Platforms, Kubernetes, AI Engineering, MLOps, Data Engineering, Enterprise Architecture, Security, or Program Delivery.
- Experience working in a forward-deployed delivery model by embedding with client SMEs, business users, and engineering teams through solution validation, production deployment, adoption, and handover.
- Strong hands-on data discovery skills, including profiling unfamiliar client data with Python, pandas, PySpark, and SQL; assessing coverage and quality; and quantifying volumes, distributions, edge cases, and business-process gaps before development.
- Ability to rapidly build thin working solutions using real client data and APIs, validate them with users, and iteratively harden them with automated tests, security controls, error handling, observability, and production support procedures.
- Experience creating business-relevant evaluation datasets with client SMEs, measuring ML or LLM quality, performing structured error analysis, grouping failures by root cause, and prioritizing improvements across releases.
- Demonstrated production ownership after go-live, including monitoring, incident diagnosis, rollback, stabilization, performance and latency troubleshooting, and resolution of user-reported issues.
- Ability to measure user adoption and business impact after launch using usage data and agreed KPIs such as time saved, error reduction, straight-through processing, quality improvement, or productivity gains.
- Willingness and ability to travel or work onsite with client teams when an engagement requires close collaboration and accelerated delivery.
Roles & Responsibilities
- Design, develop, and deploy enterprise-grade AI and data applications using Python, PySpark, Claude, SQL, APIs, and modern cloud-based data platforms.
- Engage with business and technology stakeholders to understand business objectives, data landscape, functional requirements, non-functional requirements, and delivery expectations.
- Define solution architecture, implementation roadmaps, and technical approaches that align with business goals and operational requirements.
- Architect scalable data platforms, data pipelines, and AI solutions with a strong focus on performance, security, reliability, and cost optimization.
- Develop batch, real-time, and streaming data processing solutions integrating multiple enterprise data sources and downstream applications.
- Build and operationalize Claude-powered applications, copilots, and AI workflows using prompt engineering, Retrieval-Augmented Generation (RAG), vector search, and enterprise data integration.
- Implement responsible AI practices, including governance, security, privacy, traceability, validation, and human oversight controls.
- Design and enforce engineering standards, reusable frameworks, CI/CD practices, infrastructure automation, and production deployment patterns.
- Provide technical leadership across solution design, code reviews, architecture reviews, testing, deployment, and production support activities.
- Drive platform reliability, performance tuning, observability, incident resolution, and operational excellence across data and AI workloads.
- Collaborate closely with architects, data engineers, AI specialists, platform teams, and client stakeholders to deliver production-ready solutions.
- Support solutioning, RFP responses, technical demonstrations, effort estimation, and client workshops for new opportunities.
- Develop reusable accelerators, reference architectures, implementation playbooks, and engineering best practices to improve delivery efficiency.
- Mentor engineering teams and contribute to hiring, capability development, technical assessments, and knowledge-sharing initiatives.
- Embed with client SMEs, product owners, and engineers for a defined, time-bound outcome; map the workflow, users, data, constraints, and decision points; and agree on the first useful release and measurable success criteria.
- Analyze client data early to assess coverage, quality, volumes, distributions, edge cases, and process feasibility before committing to solution design and build scope.
- Build a thin working version quickly on real data and APIs, validate it with business users, and harden it iteratively for security, reliability, maintainability, and production operations.
- Create evaluation datasets with client SMEs, perform model and LLM error analysis. and suggest improvements.
- Resolve data-access, API, identity, network, security, infrastructure, and deployment blockers in partnership with client data, platform, security, and engineering te ams.
- Own post-go-live stabilization by monitoring services, diagnosing user-reported failures, latency and cost issues, executing rollback or recovery where required, and shipping fixes.
- Measure adoption and realized business impact using usage data and agreed KPIs, and communicate results through concise dashboards, demonstrations, or executive summaries.
- Complete structured handover with runbooks, support ownership, knowledge transfer, and reusable components, test documents, and deployment patterns.
Generic Managerial Skills, If any
- Strong problem-solving and decision-making abilities with a balanced focus on business outcomes, technology strategy, security, scalability, and operational excellence.
- Excellent communication, presentation, and stakeholder management skills, with the ability to engage effectively with business, technical, and executive audiences.
- Strong consulting mindset with the ability to analyze complex challenges, recommend practical solutions, and clearly articulate architectural trade-offs.
- Proven ability to lead and influence cross-functional, distributed teams in a collaborative and fast-paced delivery environment.
- High level of ownership, accountability, attention to detail, and commitment to engineering quality and delivery excellence.
- Strong planning, prioritization, and multitasking skills with the ability to manage multiple initiatives, stakeholders, and delivery commitments simultaneously.
- Ability to mentor engineers and architects, foster knowledge sharing, and drive adoption of engineering best practices.
- Adaptability and resilience to work effectively in rapidly evolving technologies, business priorities, and client environments.
Required Skills
Senior Email Security Engineer SQL Application Developer
Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 11,520 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At Realign, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $205,000 based on 171 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Realign AI Hiring
Realign has 23 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer, AI Architect, Data Engineer. Positions span Cincinnati, OH, US, Tampa, FL, US, Irving, TX, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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