Principal Machine Learning Engineer
ConnectWise · India
- Experience7–8 yrs
- SalaryNot disclosed
- Work moderemote
- Levelexecutive
- Posted16 Sept 2026
About ConnectWise
ConnectWise is hiring in India in technology software. This role looks for around 7+ years of experience.
Skills
- Machine Learning
- Deep Learning
- Generative AI
- Supervised Fine-Tuning
- Direct Preference Optimization
- Reinforcement Fine-Tuning
- Parameter-Efficient Fine-Tuning
- LoRA
- QLoRA
- BERT
- ModernBERT
- Retrieval-Augmented Generation
- Model Evaluation
- Large Language Models
- Synthetic Data
- Knowledge Distillation
- Python
- Hugging Face Transformers
- TRL
- PEFT
- Sentence-Transformers
- scikit-learn
- SQL
- Apache Spark
- Git
- AWS
- Amazon EMR
- Amazon S3
- Amazon SageMaker
- Machine Learning Model Deployment
- Machine Learning Model Monitoring
The role
A machine learning engineer at a software product company builds machine learning models for predictive and classification use cases, develops generative AI solutions with Python, and deploys production machine learning services on AWS. The work also applies retrieval-augmented generation and Apache Spark to support data pipelines and product decisions.
Full job description
Work Type: Remote
General Summary:The Principal Machine Learning Engineer is responsible for building Machine learning models based on diverse business requirements, setting up the pipelines, and assisting in delivering thoughtful experiences for our partners. This role works in partnership with cross-functional teams to contribute to the development of cutting-edge ML solutions.
Role ResponsibilitiesBuild Machine Learning and Deep Learning models.Train models using proprietary company data.Work on predictive and classification use cases.Develop models that support multiple company products.Take models from development through production deployment.Build APIs and wrappers around ML models.Collaborate with Cloud and DevOps teams for deployment.Assists in delivering production grade machine learning services that power the ConnectWise platform and products.Works with cross-functional teams to ensure that proper data pipelines are established to ensure availability of high-quality data.Informs, influences, supports, and executes on product decisions and product launches.
Knowledge, Skills, and/or Abilities Required: Ability to work independently on projects and processes with close supervision.Broad theoretical knowledge of ML/ AI space and application development using generative AI including supervised fine-tuning, preference optimization (DPO), and reinforcement fine-tuning (RFT) of LLMs; parameter-efficient fine-tuning (LoRA/QLoRA); fine-tuning encoder models such as BERT/ModernBERT for text classification; and retrieval-augmented generation with embedding retrievers and cross-encoder rerankers.Strong grasp of model evaluation methodology (task-specific eval sets, LLM-as-judge, offline metrics, and online A/B testing) and experience building training-data, synthetic-data, and distillation pipelines for post-training.Ability to situationally adapt and understand new technology/processes as per business partner requirement.Strong programming skills in python and fluency in common libraries ( Hugging Face Transformers, TRL, PEFT, Sentence-Transformers, scikit-learn, etc.)Proficient in SQL and/or other data manipulation languages.Knowledge of big data processing tools such as Apache Spark.Proficiency in version controls systems such as Git.Knowledge of at least one cloud platform (e.g. AWS) and its relevant services (e.g. EMR, S3, and SageMaker).Ability to interpret business requirements and translate into ML deliverables.Ability to break down and communicate complex, highly technical concepts to audiences of varying technical understanding
Educational/Vocational/Previous Experience Recommendations:Bachelor's degree in CS or related field required; Master’s or PhD preferred.7+ years of relevant experienceExperience writing code (e.g. Python) and taking machine learning models to production.Experience building software on cloud computing platforms.Experience deploying, monitoring, and iterating machine learning models in production.