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Selected role

Python Engineer Generative AI

Jobs via Dice

Senior Full-time

Job Title:
Python Engineer (Generative AI)

Location:
Remote

Role Overview:
As a Python Engineer with
Generative AI
expertise, you will help clients design and implement AI-driven solutions, particularly those leveraging large language models and other generative technologies. Stationed on-site, you serve as the resident AI/ML expert on the project, working directly with the client's data scientists, product managers, and business stakeholders to
bring GenAI capabilities into their products and processes
. This could range from developing intelligent assistants and predictive models to integrating third-party AI services into existing systems. Your role is equal parts software engineer, AI researcher, and trusted advisor - ensuring the client harnesses the latest in AI effectively and ethically.

Key Responsibilities:

AI Solution Development: Build and deploy generative AI models or applications tailored to client needs. For example, you might develop a custom NLP pipeline using large language model APIs (like GPT-4) to power a chatbot, or fine-tune a generative model on the client's proprietary data to generate content or predictions. You'll write Python code to train models, optimize them, and integrate them into production systems. This could involve developing microservices that expose AI model endpoints, or creating data processing pipelines to feed the models.
Technical Consulting & Requirements Gathering: Being on-site, you will work closely with client stakeholders to identify high-impact AI opportunities. You might lead workshops to brainstorm AI use cases or review the client's pain points to see where GenAI can help. You'll help non-technical stakeholders understand what's feasible with current AI tech. Once a use-case is decided, you translate it into technical requirements - e.g., what data is needed, what model to use or build, latency considerations, etc.
Collaboration with Client Teams: Expect to interface daily with a variety of client personnel: data engineers (to access and prepare data for your models), software developers (to integrate AI components into the larger system), and product owners (to refine feature behavior). On a given day, you might pair-program with a client's developer to embed your ML model into their application, or sit with a business analyst to interpret model outputs. Your presence helps the client team gain AI competence; you might even conduct small training sessions or code walkthroughs to uplift their AI understanding.
MLOps and Lifecycle Management: You are responsible for not just building models, but ensuring they run reliably in production. This means setting up proper model deployment (perhaps on AWS Sagemaker, Azure ML, or a containerized environment), monitoring model performance/drift, and establishing retraining pipelines if needed. Onsite, you'll coordinate with the client's IT or DevOps folks to align on deployment infrastructure and security protocols. You'll also institute best practices for version control of datasets and models, experiment tracking, and documentation so that the client can maintain the AI solution long-term.
Ethical and Quality Oversight: Generative AI is powerful but can be unpredictable. Part of your role is to ensure the AI solutions are safe, unbiased, and aligned with client expectations. You set up processes for human-in-the-loop review where appropriate, handle data privacy carefully, and implement guardrails on AI outputs (for example, filtering out undesirable content from a generative model's responses). You might also assist the client in developing usage policies or user guidelines for the AI features. Essentially, you help them navigate the responsible AI considerations so the technology remains an asset, not a liability.

Technical Skills & Experience:

Python & ML Frameworks: Mastery of Python is essential, as it's the lingua franca of AI development. You have strong experience with machine learning frameworks such as TensorFlow or PyTorch for building and fine-tuning models. Familiarity with libraries for natural language processing (e.g., Hugging Face Transformers, spaCy) and data manipulation (pandas, NumPy) is expected.
Generative AI & LLMs: Hands-on experience with generative models - this could include working with Large Language Models (LLMs) like GPT, BERT or open-source equivalents, as well as other generative techniques (GANs, VAEs). You understand how to fine-tune pre-trained models, prompt engineering techniques to get optimal results from LLMs, and how to handle model constraints (context length, etc.). If you have built chatbots or content generators, highlight those projects.
Data Engineering Knowledge: Ability to handle the data pipeline for AI projects. This means comfortable with data extraction, cleaning, and transformation. Experience with big data tools (Spark, Databricks) or databases (SQL, NoSQL) to gather the necessary training data. Knowledge of how to efficiently manipulate large datasets is important for model training.
Cloud AI Services & MLOps: Familiarity with cloud-based AI services (AWS, Azure, or Google Cloud AI offerings) can be crucial. For instance, you may use AWS SageMaker, Azure Cognitive Services/OpenAI Service, or Google Cloud Platform Vertex AI as part of solutions. Experience containerizing ML models with Docker and deploying them is a plus. Understanding of MLOps concepts - CI/CD for ML, automated retraining, model monitoring - is highly valued, since you'll be embedding models into production use.
Software Engineering & APIs: You're not an isolated researcher; you deliver working software. So, solid software engineering practices are needed - version control (Git), coding standards, and writing modular, testable code. You should be able to create RESTful APIs (e.g., using Flask/FastAPI or similar) to serve model predictions, and ensure these services scale and respond within required SLAs.
Experience: Typically 4+ years in software/ML engineering, including at least 2 years focusing on AI/ML and ideally some projects with generative AI. Experience deploying an AI model in a real product or workflow is important. Prior consulting or client-facing experience is a major plus since you will be in a consultative role daily.

Soft Skills & Competencies:

Analytical & Innovative Thinking: You approach problems with a scientific mindset. When confronted with a business problem, you hypothesize which AI techniques might solve it and objectively weigh feasibility (sometimes AI is not the right solution, and you'll say so). You stay up-to-date with the fast-evolving AI landscape, bringing innovative ideas to the client - for example, suggesting how a new model or technique could improve their operations.
Communication & Education: One of your key contributions on-site is demystifying AI for others. You can explain complex ML concepts in everyday language, using analogies and visualizations. Whether it's writing an easy-to-understand report on model results or guiding a team on how to interpret AI outputs, you ensure clarity. You're also patient and enthusiastic in educating - you might run a brown-bag session for client staff on AI basics, or produce how-to documentation for the solution you deliver.
Collaboration & Teamwork: Building AI solutions often involves cross-disciplinary teamwork. You collaborate effectively with software engineers, domain experts, and project managers. You respect the knowledge each person brings - for instance, you'll work closely with a client's domain expert to understand nuances in the data that pure algorithms might miss. Onsite presence means you actively participate in team rituals (standups, planning) and you show a team-first attitude, helping out in areas beyond your direct remit when needed (like debugging an API issue that isn't strictly "AI" because it's blocking the project).
Autonomy & Initiative: Often you might be the only AI specialist on-site, which means you should be self-driven and capable of managing your work with minimal supervision. You proactively identify what needs to be done - if data is missing, you seek it out; if model accuracy is not enough, you research and try improvements. Clients will look to you for direction in AI matters, so you should be comfortable taking initiative, setting a roadmap, and driving it to completion. You manage your time and tasks efficiently, often juggling model development with meetings and knowledge transfer sessions.
Ethical Mindset and Reliability: Given the power of AI, you carry an ethical responsibility. You are conscious of bias, fairness, and privacy - and you incorporate those considerations into your work (for example, alerting the client if the training data might be biased or if an AI use-case might raise compliance issues). Furthermore, you deliver on what you promise - if you commit to a proof-of-concept by end of week, you hit that deadline or communicate early if adjustments are needed. The client trusts you as a reliable expert who balances innovation with responsibility.

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Explore the complete
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