What you'll do:
Responsibilities:
- Responsible for designing, developing, and deploying advanced machine learning models and algorithms. This includes selecting appropriate techniques, data pre-processing, feature engineering, model training, and evaluation.
- Stays up to date with the latest advancements in the field and leads research initiatives to explore novel approaches and technologies. This involves conducting experiments, evaluating new algorithms, and identifying opportunities for innovation.
- Responsible for designing the architecture of AI systems and ensuring scalability, performance, and reliability. This includes optimizing algorithms, leveraging distributed computing frameworks, and utilizing cloud services to enable efficient and effective AI solutions.
- Works closely with other teams, such as data scientists, software engineers, and product managers. You will collaborate to understand requirements, identify opportunities for AI integration, and provide technical guidance throughout the development process.
- Provides technical leadership and mentorship to junior engineers, guiding them in best practices for AI and machine learning. Review their work and provide feedback to help them grow and improve their skills.
- Oversees and guides multiple design review sessions across different projects, ensuring consistency in design choices and adherence to best practices. Act as a key mentor for the team.
- Partners with the engineering manager and team lead to establish long-term design and implementation strategies.
- Leads efforts to incorporate feedback loops and continuous improvement processes.
- Leads meetings, ensuring efficient progress tracking, issue resolution, and team coordination. Support the engineering manager in setting meeting agendas.
- Creates and delivers high-level presentations and reports to executive stakeholders, effectively communicating complex technical strategies and their impact on business goals.
- Applies and leverages data mining, data modeling, natural language processing, and machine learning to extract and analyze information from datasets
- May be involved in the design and development of solutions to complex applications problems, system administration issues, or network concerns, where applicable to the role.
What you need to bring:
Education and Experience Required:
- Master s degree in computer science, engineering, data science, machine learning, artificial intelligence, or closely related quantitative discipline.
- Typically, 10-15 years experience.
Knowledge and Skills:
- Solid understanding of fundamental AI and machine learning concepts, including supervised and unsupervised learning, deep learning, reinforcement learning, natural language processing, computer vision, and statistical modeling.
- Proficient in implementing and deploying various machine learning algorithms, such as decision trees, random forests, support vector machines, and neural networks. Knowledge of popular machine learning frameworks and libraries like TensorFlow, PyTorch, or sci-kit is required.
- Expertise in deep learning techniques, architectures, and frameworks (e.g., convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), etc.) is highly valuable.
- Strong programming skills are necessary for implementing and deploying machine learning models. Proficiency in Python, Java, or C++ is required. Ability to write clean, efficient, and maintainable code and have knowledge of software engineering best practices.
- Be skilled in preparing and cleaning data for machine learning tasks, which include data wrangling, feature extraction, dimensionality reduction, handling missing values, and addressing data quality issues.
- A solid understanding of mathematical concepts, such as linear algebra, calculus, probability theory, and statistics, is crucial for effectively designing and evaluating machine learning models.
- Proficiency in data visualization tools and techniques to analyze and present insights gained from AI and machine learning models. Create meaningful visualizations and effectively communicate complex concepts to technical and non-technical stakeholders.
- Be skilled in fine-tuning and optimizing ML/DL and transformer-based models (classification, regression, clustering, sequence modeling, embeddings, BERT/GPT-style LLMs) including custom tokenizers and domain-specific architectures.
- Expertise in architecting and building end-to-end NLP/GenAI solutions RAG pipelines, embedding models, vector databases, semantic search, summarization, conversational AI, and prompt-optimization workflows using tools like LangChain, LlamaIndex or custom frameworks.
- Expertise in deploying and scaling AI/ML products on cloud platforms (AWS/Azure/GCP), integrate LLMs into production APIs.
- Familiarity in defining safety, evaluation, and monitoring processes for reliable GenAI operations.
- Familiarity with software engineering principles, version control systems (e.g., Git), testing methodologies, and agile development practices is valuable. This helps ensure the robustness, scalability, and maintainability of AI systems.
- Stay up to date with the latest advancements in the field, follow research papers, attend conferences, and contribute to the AI community. A passion for continuous learning and a drive for innovation are essential traits.
- Experience in leading research initiatives, publishing research papers, or contributing to open-source projects in AI.
- Experience in guiding and mentoring other engineers.
- Strong communication skills are necessary to collaborate with cross-functional teams effectively, explain complex AI concepts to non-technical stakeholders, and provide technical leadership and mentorship to junior engineers.
Artificial Intelligence Technologies, Cross Domain Knowledge, Data Engineering, Data Science, Design Thinking, Development Fundamentals, Full Stack Development, IT Performance, Machine Learning Operations, Scalability Testing, Security-First Mindset
