Machine Learning Engineer
Lingaro · India
- Hybrid
- Full-time
- $150,000 / year
- India
Job highlights
- Implement ML models and GenAI solutions into production.
- Design and manage industrialized data processing pipelines.
- Apply best practices in ML/LLM operations and lifecycle.
- Utilize MLOps/LLMOps tools and cloud platforms like Azure/GCP.
- Collaborate with top engineers in a flexible work environment.
About the role
Machine Learning Engineer - AI Engineering Team
Join Lingaro's Data Science and AI Competency Center as part of the AI Engineering team. We are looking for a Machine Learning Engineer to implement ML models into production, design and deliver GenAI solutions, and drive practical, innovative implementations of LLM/ML/AI automation for scale and efficiency.
Key Responsibilities:
- Design, deliver, and manage industrialized processing pipelines.
- Define and implement best practices in ML model lifecycle and ML operations/LLM operations.
- Implement AI/MLOps/LLMOps frameworks and support Data Science teams.
- Gather and apply knowledge on modern techniques, tools, and frameworks in ML Architecture and Operations.
- Gather technical requirements and estimate planned work.
- Present solutions, concepts, and results to internal and external clients.
- Create technical documentation.
What We're Looking For:
Must Have:
- 5+ years of Data engineering experience, with the last 3 years in building Data processing.
- 5+ years of experience in production-ready Python code development (e.g., microservices, APIs).
- 3+ years of experience in production-ready ML-related code development.
- 1+ years of experience with GenAI (ChatGPT, Gemini, RAGs, prompt engineering).
- Practical experience in MLOps/LLMOps tools like AzureML/AzureAI.
- Practical experience with Databricks.
- Good understanding of ML/AI concepts (algorithms, frameworks, metrics, lifecycle, architectures).
- Good understanding of Cloud concepts and architectures, preferably Azure or GCP.
- Experience in at least one of the following: Data Warehouse, Data Lake, Data Integration, Data Governance, Machine Learning, Deep Learning, MLOps.
- Practical experience in Spark/PySpark and Hive within Big Data Platforms (Databricks, EMR, or similar).
- Experience in designing and implementing data pipelines.
- Good communication skills.
- Ability to work in a team and support others.
- Taking responsibility for tasks and deliverables.
- Great problem-solving skills and critical thinking.
- Fluency in written and spoken English.
What Will Set You Apart:
- Experience in designing and programming ML algorithms and data processing pipelines using Python.
- Good understanding of CI/CD and DevOps concepts, and experience with tools like GitHub Actions, GitLab, or Azure DevOps.
- Experience in productizing ML solutions using technologies like Spark/Databricks or Docker/Kubernetes.
Missing one or two qualifications? We still want to hear from you! If you bring a positive mindset, we'll provide an environment where you feel valued and empowered to learn and grow.
What We Offer:
- Stable employment since 2008 with 1500+ talents across 7 global sites.
- “Office as an option” model: choose to work remotely or in the office.
- Flexibility in working hours and contract type.
- Comprehensive online onboarding with a “Buddy” from day 1.
- Cooperation with top-tier engineers and experts.
- Unlimited access to Udemy learning platform.
- Certificate training programs (500+ technology certificates yearly).
- Upskilling support: capability development programs, Competency Centers, knowledge sharing sessions, webinars, 110+ training opportunities yearly.
- Internal Gallup Certified Strengths Coach.
- Growth opportunities: 76% of managers are internal promotions.
- Diverse, inclusive, and values-driven community.
- Autonomy and trust in your ideas.
- Referral bonuses.
- Well-being and health activities.
- Charitable and environmental initiatives.
- Modern office equipment.
Key skills/competency:
- Machine Learning Engineer
- AI Engineering
- GenAI Solutions
- LLMOps
- MLOps
- Data Pipelines
- Python
- Databricks
- Spark
- AzureAI
Skills & topics
- Machine Learning Engineer
- Data Engineering
- Python
- GenAI
- LLMOps
- MLOps
- Databricks
- Spark
- AzureAI
- Cloud Computing
- AI Engineering
- Data Pipelines
How to get hired
- Tailor your resume: Highlight your 5+ years of data engineering, 3+ years in Python, and 1+ year GenAI experience. Emphasize MLOps/LLMOps and cloud platform skills.
- Showcase your skills: Prepare to discuss practical experience with Databricks, Spark/PySpark, and implementing data pipelines. Detail your contributions to production-ready ML code.
- Demonstrate problem-solving: Be ready to share examples of your critical thinking, ability to take responsibility, and teamwork in previous roles.
- Communicate effectively: Ensure your English fluency is evident and prepare to present solutions and concepts clearly.
- Express enthusiasm for growth: Highlight your proactive approach and eagerness to learn within Lingaro's supportive environment.
Technical preparation
Master production-ready Python coding and ML frameworks.,Demonstrate practical MLOps/LLMOps and cloud platform skills.,Showcase experience with Databricks, Spark, and data pipelines.,Build and deploy GenAI solutions like RAGs and LLMs.
Behavioral questions
Describe a complex ML problem you solved.,How do you ensure ML model reliability in production?,Share an example of teamwork and supporting others.,How do you approach technical documentation and client presentations?
Frequently asked questions
- What are the key responsibilities of a Machine Learning Engineer at Lingaro?
- As a Machine Learning Engineer at Lingaro, you'll implement ML models into production, design and deliver GenAI solutions, build industrialized processing pipelines, and define best practices for ML/LLM operations. You'll also gather technical requirements, present solutions, and create technical documentation.
- What specific GenAI experience is required for this Machine Learning Engineer role?
- Lingaro requires at least 1 year of experience with GenAI technologies such as ChatGPT, Gemini, RAGs, and prompt engineering. This practical experience is crucial for developing innovative AI solutions.
- Which MLOps/LLMOps tools and cloud platforms are preferred for this Machine Learning Engineer position?
- Practical experience with MLOps/LLMOps tools like AzureML/AzureAI is essential. Familiarity with cloud concepts and architectures, preferably on Azure or GCP, is also highly valued for this role.
- What level of Python development experience is needed for the Machine Learning Engineer role?
- We require at least 5 years of experience in production-ready Python code development, including areas like microservices and APIs. Additionally, 3 years of experience specifically in production-ready ML-related code development is necessary.
- Does Lingaro offer remote work options for the Machine Learning Engineer position?
- Yes, Lingaro offers an 'Office as an option' model, allowing you to choose to work remotely or from the office, depending on your location and preference.
- What kind of learning and development opportunities are available for a Machine Learning Engineer at Lingaro?
- Lingaro provides unlimited access to the Udemy learning platform, certificate training programs, upskilling support through capability development programs, Competency Centers, knowledge sharing sessions, and numerous other training opportunities annually.
- What is the expected experience with data processing and big data platforms for this role?
- We are looking for at least 5 years of data engineering experience, with the last 3 focused on building data processing. Practical experience with Spark/PySpark and Hive within Big Data Platforms like Databricks or EMR is also required.
- How important are communication and teamwork skills for a Machine Learning Engineer at Lingaro?
- Good communication skills, the ability to work effectively in a team, and support for colleagues are highly valued. Strong problem-solving skills and critical thinking are also essential for success in this role.