Applied AI Engineer Jobs
Build Real Products, Not Papers.

Applied AI engineers build production systems that use AI to solve real business problems. Unlike research roles, you're not publishing papers - you're shipping products. Day-to-day work includes designing ML pipelines, integrating LLMs into applications, optimizing model inference for scale, building APIs that serve predictions, and collaborating with product teams. You own the full lifecycle: from understanding requirements to deploying and monitoring production models.

Research roles focus on advancing the field through novel algorithms and publications. Applied roles focus on deploying existing techniques to solve business problems at scale. Research optimizes for novelty and accuracy improvements. Applied optimizes for reliability, latency, cost, and business impact. Applied engineers need stronger software engineering skills - version control, testing, CI/CD, system design. Research engineers need deeper mathematical foundations. Most companies need far more applied engineers than researchers.

Core technical skills: Python, ML frameworks (PyTorch, TensorFlow), LLM APIs and RAG patterns, cloud platforms (AWS/GCP/Azure), Docker and containerization, API development, SQL and vector databases. Production skills: MLOps and model deployment, monitoring and observability, performance optimization, system design for ML. Soft skills: translating business problems to technical solutions, communicating with non-technical stakeholders, pragmatic decision-making about build vs buy.

In the US market: Junior/entry-level (0-2 years): $120K-$160K base. Mid-level (2-5 years): $160K-$220K base. Senior (5+ years): $200K-$300K+ base. Staff/Principal: $280K-$400K+ base. Top tech companies (FAANG, well-funded startups) pay at the higher end, often with significant equity. Remote roles and companies outside major tech hubs typically pay 70-85% of these ranges. Total compensation including equity can be 1.5-2x base salary at top companies.

The market in 2026 is broad. Big tech (Google, Meta, Amazon, Microsoft) have large applied AI teams. AI-native companies (OpenAI, Anthropic, Cohere, Scale AI) hire heavily. Enterprise software companies (Salesforce, Datadog, Snowflake) are embedding AI features. Startups across every vertical need applied AI talent. Traditional industries (finance, healthcare, manufacturing) are building internal AI teams. Look for roles titled: AI Engineer, Applied ML Engineer, ML Platform Engineer, LLM Engineer, or AI Solutions Engineer.

Start by building production-quality projects that demonstrate end-to-end skills. Don't just train models - deploy them with proper monitoring, build APIs around them, handle edge cases. Open source contributions to ML tools show you can work in production codebases. If you're a software engineer, leverage that: your deployment and system design skills are valuable. Consider targeting companies building AI-powered products rather than pure ML teams - they value production engineering skills highly. Get specific guidance on your transition path through 1:1 coaching.

Not necessarily. While some programming experience is helpful, many of my clients have successfully transitioned from web development, data science, or other technical backgrounds. We'll assess your current skills during our strategy call and create a personalized plan that meets you where you are.

Most clients invest 10-15 hours per week, but this can be flexible based on your schedule. We'll have weekly 1:1 calls plus time for you to work on projects and learning. The key is consistency. Regular, focused effort beats occasional marathons.

You become a member of the AI Native Engineer community, and you stay and keep going. Career transitions take different amounts of time for everyone, and I'm not going to abandon you if things take longer. You get ongoing support through good times and bad.

Online courses give you content. 1:1 coaching gives you a personalized roadmap, direct feedback on your work, career strategy, interview prep, and accountability. You get answers to your specific questions and guidance tailored to your unique situation instead of generic advice meant for everyone.

Investment details are discussed during the 30-minute strategy call, where we'll assess your goals and create a custom plan. The program is designed to pay for itself quickly through your increased salary. Most AI engineers see a 20-50% pay increase.

Absolutely. Most of my clients work full-time and make steady progress. We'll schedule calls at times that work for you and create a realistic plan that fits your schedule. Consistency matters more than intensity.