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About This Role
Role Overview
We are seeking a highly experienced Agentic AI Technology Lead to design, architect, and deliver next\-generation AI\-driven business applications. This role requires a hands\-on technical leader who can translate complex business needs into scalable AI solutions using modern LLM ecosystems such as ChatGPT (OpenAI), Gemini, and other enterprise AI platforms. The ideal candidate will bring deep expertise in AI application design, prompt engineering, and full\-stack AI solution development, combined with strong leadership experience managing globally distributed development teams in fast\-paced, deadline\-driven environments.
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Key Responsibilities### AI Solution Architecture \& Development
- Design, develop, and deploy agentic AI applications leveraging platforms such as OpenAI (ChatGPT), Google Gemini, and related LLM ecosystems
- Architect end\-to\-end AI\-driven business workflows, including orchestration of multi\-agent systems and tool integrations
- Lead the development of AI\-enabled solutions using C\+\+, TypeScript, and API\-based architectures
- Apply advanced prompt engineering techniques to optimize AI outputs, accuracy, and contextual performance
- Design intuitive user interfaces and application experiences for AI\-powered business tools
### Technical Leadership \& Delivery
- Act as the technical lead for AI initiatives, making decisions on architecture, tooling, and implementation strategies
- Manage and coordinate offshore development teams, including non\-native English\-speaking engineers, ensuring clarity, efficiency, and delivery quality
- Drive execution within tight timelines and aggressive delivery schedules
- Provide hands\-on support as needed in coding, debugging, and system integration
### Business Engagement \& Process Analysis
- Collaborate directly with business stakeholders and subject matter experts (SMEs) to:
+ Analyze existing business processes
+ Identify automation and AI transformation opportunities
+ Translate business requirements into technical specifications
- Operate effectively in a contractor/consultant capacity, adapting to varying client environments and expectations
### Security, Compliance \& Data Governance
- Ensure strict adherence to security protocols and enterprise standards
- Design and implement solutions that securely handle and process PII (Personally Identifiable Information)
- Apply best practices for data privacy, access control, and risk mitigation in AI systems
- Maintain compliance with internal and regulatory requirements for AI usage and data handling
Required Qualifications/Skills
- Bachelor’s degree in Computer Science, Information Technology, or related field.
Technical Expertise* Proven experience building AI applications using:
+ OpenAI (ChatGPT), Gemini, or equivalent LLM platforms
- Strong programming skills in:
+ C\+\+
+ TypeScript
+ Python
- Demonstrated expertise in:
+ Prompt Engineering
+ API integrations and microservices architecture
+ AI/LLM orchestration and agent\-based systems
### AI \& Application Development
- Experience designing and developing AI business applications and intelligent automation use cases
- Strong understanding of:
+ Conversational AI design
+ Context management and memory handling in AI systems
- Experience in application UI/UX design for AI\-driven interfaces is strongly preferred
- Experience managing offshore/global development teams
- Ability to communicate effectively across language and cultural barriers
- Proven track record of delivering high\-quality solutions under tight deadlines
### Leadership \& Delivery
- Experience managing offshore/global development teams
- Ability to communicate effectively across language and cultural barriers
- Proven track record of delivering high\-quality solutions under tight deadlines
### Business \& Consulting Skills
- Strong capability in business process analysis and solutioning
- Experience working directly with business stakeholders and SMEs
- Comfortable operating in contract\-based or consulting roles
### Security \& Compliance
- Deep understanding of:
+ Secure application design
+ PII data handling best practices
+ Enterprise security protocols
- Experience working within regulated environments is a plus
Preferred Qualifications* Experience with agent frameworks (e.g., Copilot, Semantic Kernel, custom orchestration frameworks)
- Familiarity with multi\-agent architectures and RAG (Retrieval\-Augmented Generation) systems
- Exposure to cloud platforms (Azure, GCP, AWS) supporting AI workloads
- Experience in enterprise automation ecosystems (RPA \+ AI integration)
Locations:Pittsburgh, PA
About Techstra Solutions
Techstra Solutions is a certified woman\-owned (WBENC) management consulting firm specializing in strategy, technology, and implementation services for large organizations undergoing digital and talent transformation. Our experienced team partners with clients to co\-create innovative solutions in applications, data, AI, and automation that accelerate measurable, sustainable change. From advisory consulting through technical execution, we are dedicated to driving world\-class business solutions that fit your strategic requirements and deliver results. For more information: www.techstrasolutions.com
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Equal Employment Opportunity Statement
Techstra Solutions is an equal opportunity employer. The Company makes its decisions on merit, and its policy of equal opportunity prohibits discrimination in all phases of the employment process, including, but not limited to, recruitment, hiring, promotion, selection, transfer, demotion, layoff, termination, compensation, benefits, and other terms and conditions of employment. The policy of equal opportunity applies without regard to race, color, creed, religion, gender, sexual orientation, gender identification, pregnancy, marital status, national origin, ancestry, age, disability that can reasonably be accommodated without undue hardship, military status, veteran status, genetic predisposition or carrier status, alienage or citizenship, domestic partnership status, arrest or conviction record, status as a victim of domestic violence, or any other protected categories under federal, state, or local law. The Company also prohibits discrimination or harassment based upon the perception that a person has, or is associated with a person who has, any of these characteristics.
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Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At TECHSTRA SOLUTIONS, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
TECHSTRA SOLUTIONS AI Hiring
TECHSTRA SOLUTIONS has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Pittsburgh, PA, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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.
Frequently Asked Questions
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