Commtel Networks
Website:
commtelnetworks.com
Job details:
Job Title: AI/ML Engineer - II
Department: Solutions R&D
Reports To: Senior ML Engineer / Lead Software Architect – Solutions R&D
Location: Juinagar, Navi Mumbai, Maharashtra
Employment Type: Full-Time
Role Overview: The AI/ML Engineer - II is a hands-on contributor within Solutions R&D, responsible for developing, implementing, and supporting Machine Learning, Deep Learning, and Agentic AI components under the guidance of Lead/Senior Engineers. The role focuses on building and validating models, developing data pipelines, and helping integrate AI modules into real-world telecom, safety, and security (iTSS) systems.
The role suits an engineer with a solid ML/DL foundation who is ready to take ownership of well-scoped problems, iterate on models, and grow toward independent design responsibility. The engineer works closely with domain experts, software teams, and data engineers, contributing to real-time analytics, predictive maintenance, and knowledge-automation efforts.
Key Responsibilities:
• ML/DL Model Development
o Develop and validate ML and DL models using Python, PyTorch, TensorFlow, scikit-learn, XGBoost, etc., under senior guidance.
o Build and iterate on models for classification, regression, anomaly detection, prediction, and NLP tasks. o Contribute to LLM-based and Agentic AI systems for domain knowledge automation and workflows.
o Assist in experimenting with time-series and sequence-modeling architectures.
• RAG, Agentic AI & LLM Integration
o Implement and maintain components of Retrieval-Augmented Generation (RAG) systems for knowledge-base operations, document analysis, and semantic search.
o Deploy and evaluate local LLMs using frameworks such as HuggingFace Transformers, LangChain, LlamaIndex, or equivalent.
o Support the development and testing of agent-based workflows involving multi-step reasoning and tool usage.
o Help optimize LLM pipelines for latency, throughput, and accuracy.
• Data Engineering & Real-Time Analytics
o Work with time-series data, structured and unstructured datasets, logs, sensor streams, and industrial telemetry.
o Build data preprocessing, feature-engineering, and augmentation pipelines for noisy or incomplete data.
o Support integration of AI models into real-time data-processing systems alongside backend/streaming teams.
• Model Evaluation & Deployment
o Evaluate models using cross-validation and domain-aligned metrics.
o Package and deploy models into production environments (Docker, REST APIs, microservices).
o Assist in monitoring, observability, and retraining based on performance drift and new data.
• Prototyping & Learning
o Prototype AI-driven tools and methods for predictive maintenance, anomaly detection, and knowledge automation.
o Keep up with developments in ML/DL, time-series modeling, and agent-based systems.
• Collaboration & Documentation
o Work with domain SMEs, software teams, and senior engineers.
o Document methodologies, results, experiment logs, and model artifacts clearly.
o Communicate findings to technical/non-technical stakeholders.
Qualifications & Experience:
• Educational Requirements:
o Bachelor's degree in ML, AI, Computer Science, Data Science, or related fields + 2–4 years hands-on ML/DL experience.
o Master's degree + 1–3 years hands-on ML/DL experience.
• Professional Experience:
o Practical experience building and validating ML/DL models on real datasets.
o Some exposure to streaming/time-series or IoT sensor data is an advantage.
o Familiarity with RAG, LLMs, or agentic AI frameworks is a plus.
Skills & Competencies:
• Technical Skills (Must-Have)
o Programming § Solid proficiency in Python and core ML/DL libraries: PyTorch or TensorFlow, scikit-learn, NumPy, Pandas.
o Deep Learning § Working knowledge of CNNs, RNN/LSTM/GRU, Transformers, and Autoencoders.
o LLMs & Modern AI § Hands-on experience with at least one of: HuggingFace, LangChain/LlamaIndex, RAG architectures, or local model deployment. Basic understanding of prompt engineering and model evaluation.
o Data Handling Comfort working with time-series, structured/unstructured data, and logs. Feature engineering and preprocessing for real datasets.
o Deployment § Familiarity with Docker, APIs, and running models on GPU.
• Analytical & Behavioral Competencies
o Sound mathematical and statistical intuition.
o Patience and attention to detail for iterative experimentation.
o Curiosity and willingness to learn advanced AI techniques.
o Clear written and verbal communication.
o Ability to collaborate in multidisciplinary teams and take direction well.
• Preferred Attributes (Nice-to-Have):
o Experience with industrial IoT / telecom datasets or iTSS systems.
o Exposure to predictive-maintenance use-cases.
o Vector databases (FAISS, Qdrant, Milvus, Chroma).
o Edge AI / model optimization awareness (quantization, pruning).
o Familiarity with MLOps practices (CI/CD, monitoring).
o LLM fine-tuning or multi-agent system exposure.
Key Interfaces:
• Internal: Senior ML Engineer, Lead Software Architect, HOD-Solutions R&D, Product Management, QA/QC, and Infrastructure teams.
• External: Domain Experts, Internal and External Customers.
Career Path:
With demonstrated impact, this role progresses toward:
• Senior ML Engineer
• Lead ML Engineer
• AI/ML Architect
Summary JD (AI/ML Engineer - II):
Must-Have Skills:
- Solid Python + core ML/DL libraries (PyTorch/TensorFlow, scikit-learn).
- Hands-on model building for anomaly detection, prediction, or NLP.
- Working exposure to time-series / unstructured data.
- Some experience with LLMs or RAG pipelines.
- Ability to iterate, test, and refine models under guidance.
- Good analytical and communication skills.
Nice-to-Have Skills:
- Vector databases & knowledge-retrieval systems.
- IoT / industrial sensor data experience.
- Predictive-maintenance exposure.
- MLOps / model-optimization awareness.
- LLM fine-tuning or multi-agent experience.
Click on Apply to know more.