Senior_Machine_Learning_Engineer

We are #Hiring: #Senior_Machine_Learning_Engineer

Job Title : #Senior_Machine_Learning_Engineer

Location : PAN India - ( Hybrid )

Total Yrs. of Experience : 7+ Years

️ Duration: Contract to Hire (C2H)

Notice Period: Immediate Joiners to 30 Days

Job description*:

Embedded ML engineers who translate complex business and data challenges into production-ready ML solutions. In this FDE role, the engineer manages the full ML lifecycle b from opportunity discovery and feasibility assessment through model deployment and ongoing monitoring b with strong ownership at every stage.

The role demands both deep technical capability and the ability to communicate model decisions, trade-offs, and constraints clearly to non-technical stakeholders, building confidence in AI solutions through transparency and measurable outcomes.

#REQUIRED SKILLS & EXPERTISE:

Python as primary ML language; PyTorch or TensorFlow; scikit-learn for classical ML and baselines

Tree-based models (XGBoost, LightGBM, Random Forests) and deep learning architectures(CNNs, RNNs, Transformers)

b):

Exploratory data analysis and visualization: pandas, matplotlib, seaborn, or Plotly for insight derivation

b):

SQL and PySpark/Databricks for large-scale data processing; Parquet and similar analytical formats

b):

MLOps: MLflow or equivalent for experiment tracking and model lifecycle; Docker; REST/gRPCAP Is for model serving

b):

LLMs, RAG, fine-tuning, prompt engineering, and hybrid AI/ML architectures; understanding of when each approach applies

#CORE RESPONSIBILITIES:

Identify ML opportunities in customer processes; profile data quality and availability

Prototype rapidly to validate technical feasibility before full model investment; communicate what is and is not achievable given data constraints

b):

Design, implement, and optimize machine learning algorithms, data pipelines, and AI services for scalable production deployment

b):

Run experiments, evaluate models, and deploy to cloud environments with robust observability, monitoring, and drift detection

b):

Collaborate with engineering, product, and architecture teams; explain results and trade-offs to both technical and business audiences

b):

Ensure responsible AI practices: data governance, PII compliance, auditability, and bias awareness throughout the model lifecycle

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Thanks & Regards

Sreekanth B

Burgeon IT Services

Ph No: +91-9391420349

Email:

Website: www.burgeonits.com

Shirisha. Ch Sona P @Thota Harika Ramya Sarige k Nitusha Kalyani R Sudha Yadav Veeraboina Syed Nazima Vara lakshmi kota Sushma Gowrishatti Golagani Saikumar Shiva Kumar Rajkiran G Srikanth Vicky Khaja Mansoor Ahmed Kiran Gomasa Nagarjuna Duggineni vinay goud Masthanvali SHAIK Nanda Kishore Kumar Lakkakula Laxmi Narayana (Raja) Arraganti palley Mahathi Navya Tumuluri BURGEON IT SERVICES